diff --git a/docs/index.html b/docs/index.html
index fc880840..053f0de5 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -7,7 +7,7 @@
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
 <meta name="author" content="Vijay Janapa Reddi">
-<meta name="dcterms.date" content="2024-08-17">
+<meta name="dcterms.date" content="2024-08-18">
 
 <title>Machine Learning Systems</title>
 <style>
@@ -648,7 +648,7 @@ <h1 class="title">Machine Learning Systems</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">August 17, 2024</p>
+      <p class="date">August 18, 2024</p>
     </div>
   </div>
   
diff --git a/docs/references.html b/docs/references.html
index 3f0a2329..817f6f0f 100644
--- a/docs/references.html
+++ b/docs/references.html
@@ -1350,7 +1350,7 @@ <h1 class="title">References</h1>
 in Theoretical Computer Science</em> 9 (3-4): 211–407. <a href="https://doi.org/10.1561/0400000042">https://doi.org/10.1561/0400000042</a>.
 </div>
 <div id="ref-ebrahimi2014review" class="csl-entry" role="listitem">
-Ebrahimi, Khosrow, Gerard F. Jones, and Amy S. Fleischer. 2014. <span>��A
+Ebrahimi, Khosrow, Gerard F. Jones, and Amy S. Fleischer. 2014. <span>“A
 Review of Data Center Cooling Technology, Operating Conditions and the
 Corresponding Low-Grade Waste Heat Recovery Opportunities.”</span>
 <em>Renewable Sustainable Energy Rev.</em> 31 (March): 622–38. <a href="https://doi.org/10.1016/j.rser.2013.12.007">https://doi.org/10.1016/j.rser.2013.12.007</a>.
diff --git a/docs/search.json b/docs/search.json
index 75503270..4e272443 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -3799,7 +3799,7 @@
     "href": "references.html",
     "title": "References",
     "section": "",
-    "text": "Abadi, Martin, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya\nMironov, Kunal Talwar, and Li Zhang. 2016. “Deep Learning with\nDifferential Privacy.” In Proceedings of the 2016 ACM SIGSAC\nConference on Computer and Communications Security, 308–18. CCS\n’16. New York, NY, USA: ACM. https://doi.org/10.1145/2976749.2978318.\n\n\nAbdelkader, Ahmed, Michael J. Curry, Liam Fowl, Tom Goldstein, Avi\nSchwarzschild, Manli Shu, Christoph Studer, and Chen Zhu. 2020.\n“Headless Horseman: Adversarial Attacks on Transfer\nLearning Models.” In ICASSP 2020 - 2020 IEEE International\nConference on Acoustics, Speech and Signal Processing (ICASSP),\n3087–91. IEEE. https://doi.org/10.1109/icassp40776.2020.9053181.\n\n\nAddepalli, Sravanti, B. S. Vivek, Arya Baburaj, Gaurang Sriramanan, and\nR. Venkatesh Babu. 2020. “Towards Achieving Adversarial Robustness\nby Enforcing Feature Consistency Across Bit Planes.” In 2020\nIEEE/CVF Conference on Computer Vision and Pattern Recognition\n(CVPR), 1020–29. IEEE. https://doi.org/10.1109/cvpr42600.2020.00110.\n\n\nAdolf, Robert, Saketh Rama, Brandon Reagen, Gu-yeon Wei, and David\nBrooks. 2016. “Fathom: Reference Workloads for Modern\nDeep Learning Methods.” In 2016 IEEE International Symposium\non Workload Characterization (IISWC), 1–10. IEEE; IEEE. https://doi.org/10.1109/iiswc.2016.7581275.\n\n\nAgarwal, Alekh, Alina Beygelzimer, Miroslav Dudı́k, John Langford, and\nHanna M. Wallach. 2018. “A Reductions Approach to Fair\nClassification.” In Proceedings of the 35th International\nConference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm,\nSweden, July 10-15, 2018, edited by Jennifer G. Dy and Andreas\nKrause, 80:60–69. Proceedings of Machine Learning Research. PMLR. http://proceedings.mlr.press/v80/agarwal18a.html.\n\n\nAgnesina, Anthony, Puranjay Rajvanshi, Tian Yang, Geraldo Pradipta,\nAustin Jiao, Ben Keller, Brucek Khailany, and Haoxing Ren. 2023.\n“AutoDMP: Automated DREAMPlace-Based Macro\nPlacement.” In Proceedings of the 2023 International\nSymposium on Physical Design, 149–57. ACM. https://doi.org/10.1145/3569052.3578923.\n\n\nAgrawal, Dakshi, Selcuk Baktir, Deniz Karakoyunlu, Pankaj Rohatgi, and\nBerk Sunar. 2007. “Trojan Detection Using\nIC Fingerprinting.” In 2007 IEEE Symposium on\nSecurity and Privacy (SP ’07), 29–45. Springer; IEEE. https://doi.org/10.1109/sp.2007.36.\n\n\nAhmadilivani, Mohammad Hasan, Mahdi Taheri, Jaan Raik, Masoud\nDaneshtalab, and Maksim Jenihhin. 2024. “A Systematic Literature\nReview on Hardware Reliability Assessment Methods for Deep Neural\nNetworks.” ACM Comput. Surv. 56 (6): 1–39. https://doi.org/10.1145/3638242.\n\n\nAledhari, Mohammed, Rehma Razzak, Reza M. Parizi, and Fahad Saeed. 2020.\n“Federated Learning: A Survey on Enabling\nTechnologies, Protocols, and Applications.” #IEEE_O_ACC#\n8: 140699–725. https://doi.org/10.1109/access.2020.3013541.\n\n\nAlghamdi, Wael, Hsiang Hsu, Haewon Jeong, Hao Wang, Peter Michalak,\nShahab Asoodeh, and Flavio Calmon. 2022. “Beyond Adult and\nCOMPAS: Fair Multi-Class Prediction via\nInformation Projection.” Adv. Neur. In. 35: 38747–60.\n\n\nAltayeb, Moez, Marco Zennaro, and Marcelo Rovai. 2022.\n“Classifying Mosquito Wingbeat Sound Using\nTinyML.” In Proceedings of the 2022 ACM\nConference on Information Technology for Social Good, 132–37. ACM.\nhttps://doi.org/10.1145/3524458.3547258.\n\n\nAmiel, Frederic, Christophe Clavier, and Michael Tunstall. 2006.\n“Fault Analysis of DPA-Resistant Algorithms.”\nIn International Workshop on Fault Diagnosis and Tolerance in\nCryptography, 223–36. Springer.\n\n\nAnsel, Jason, Edward Yang, Horace He, Natalia Gimelshein, Animesh Jain,\nMichael Voznesensky, Bin Bao, et al. 2024. “PyTorch\n2: Faster Machine Learning Through Dynamic Python Bytecode\nTransformation and Graph Compilation.” In Proceedings of the\n29th ACM International Conference on Architectural Support for\nProgramming Languages and Operating Systems, Volume 2, edited by\nHanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence\nd’Alché-Buc, Emily B. Fox, and Roman Garnett, 8024–35. ACM. https://doi.org/10.1145/3620665.3640366.\n\n\nAnthony, Lasse F. Wolff, Benjamin Kanding, and Raghavendra Selvan. 2020.\nICML Workshop on Challenges in Deploying and monitoring Machine Learning\nSystems.\n\n\nAntol, Stanislaw, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv\nBatra, C. Lawrence Zitnick, and Devi Parikh. 2015.\n“VQA: Visual Question Answering.”\nIn 2015 IEEE International Conference on Computer Vision\n(ICCV), 2425–33. IEEE. https://doi.org/10.1109/iccv.2015.279.\n\n\nAntonakakis, Manos, Tim April, Michael Bailey, Matt Bernhard, Elie\nBursztein, Jaime Cochran, Zakir Durumeric, et al. 2017.\n“Understanding the Mirai Botnet.” In 26th USENIX\nSecurity Symposium (USENIX Security 17), 1093–1110.\n\n\nArdila, Rosana, Megan Branson, Kelly Davis, Michael Kohler, Josh Meyer,\nMichael Henretty, Reuben Morais, Lindsay Saunders, Francis Tyers, and\nGregor Weber. 2020. “Common Voice: A\nMassively-Multilingual Speech Corpus.” In Proceedings of the\nTwelfth Language Resources and Evaluation Conference, 4218–22.\nMarseille, France: European Language Resources Association. https://aclanthology.org/2020.lrec-1.520.\n\n\nArifeen, Tooba, Abdus Sami Hassan, and Jeong-A Lee. 2020.\n“Approximate Triple Modular Redundancy: A\nSurvey.” #IEEE_O_ACC# 8: 139851–67. https://doi.org/10.1109/access.2020.3012673.\n\n\nAsonov, D., and R. Agrawal. 2004. “Keyboard Acoustic\nEmanations.” In IEEE Symposium on Security and Privacy, 2004.\nProceedings. 2004, 3–11. IEEE; IEEE. https://doi.org/10.1109/secpri.2004.1301311.\n\n\nAteniese, Giuseppe, Luigi V. Mancini, Angelo Spognardi, Antonio Villani,\nDomenico Vitali, and Giovanni Felici. 2015. “Hacking Smart\nMachines with Smarter Ones: How to Extract Meaningful Data\nfrom Machine Learning Classifiers.” Int. J. Secur. Netw.\n10 (3): 137. https://doi.org/10.1504/ijsn.2015.071829.\n\n\nAttia, Zachi I., Alan Sugrue, Samuel J. Asirvatham, Michael J. Ackerman,\nSuraj Kapa, Paul A. Friedman, and Peter A. Noseworthy. 2018.\n“Noninvasive Assessment of Dofetilide Plasma Concentration Using a\nDeep Learning (Neural Network) Analysis of the Surface\nElectrocardiogram: A Proof of Concept Study.”\nPLoS One 13 (8): e0201059. https://doi.org/10.1371/journal.pone.0201059.\n\n\nAygun, Sercan, Ece Olcay Gunes, and Christophe De Vleeschouwer. 2021.\n“Efficient and Robust Bitstream Processing in Binarised Neural\nNetworks.” Electron. Lett. 57 (5): 219–22. https://doi.org/10.1049/ell2.12045.\n\n\nBai, Tao, Jinqi Luo, Jun Zhao, Bihan Wen, and Qian Wang. 2021.\n“Recent Advances in Adversarial Training for Adversarial\nRobustness.” arXiv Preprint arXiv:2102.01356.\n\n\nBains, Sunny. 2020. “The Business of Building Brains.”\nNature Electronics 3 (7): 348–51. https://doi.org/10.1038/s41928-020-0449-1.\n\n\nBamoumen, Hatim, Anas Temouden, Nabil Benamar, and Yousra Chtouki. 2022.\n“How TinyML Can Be Leveraged to Solve Environmental\nProblems: A Survey.” In 2022 International\nConference on Innovation and Intelligence for Informatics, Computing,\nand Technologies (3ICT), 338–43. IEEE; IEEE. https://doi.org/10.1109/3ict56508.2022.9990661.\n\n\nBank, Dor, Noam Koenigstein, and Raja Giryes. 2023.\n“Autoencoders.” Machine Learning for Data Science\nHandbook: Data Mining and Knowledge Discovery Handbook, 353–74.\n\n\nBannon, Pete, Ganesh Venkataramanan, Debjit Das Sarma, and Emil Talpes.\n2019. “Computer and Redundancy Solution for the Full Self-Driving\nComputer.” In 2019 IEEE Hot Chips 31 Symposium (HCS),\n1–22. IEEE Computer Society; IEEE. https://doi.org/10.1109/hotchips.2019.8875645.\n\n\nBarenghi, Alessandro, Guido M. Bertoni, Luca Breveglieri, Mauro\nPellicioli, and Gerardo Pelosi. 2010. “Low Voltage Fault Attacks\nto AES.” In 2010 IEEE International Symposium on\nHardware-Oriented Security and Trust (HOST), 7–12. IEEE; IEEE. https://doi.org/10.1109/hst.2010.5513121.\n\n\nBarroso, Luiz André, Urs Hölzle, and Parthasarathy Ranganathan. 2019.\nThe Datacenter as a Computer: Designing Warehouse-Scale\nMachines. Springer International Publishing. https://doi.org/10.1007/978-3-031-01761-2.\n\n\nBau, David, Bolei Zhou, Aditya Khosla, Aude Oliva, and Antonio Torralba.\n2017. “Network Dissection: Quantifying\nInterpretability of Deep Visual Representations.” In 2017\nIEEE Conference on Computer Vision and Pattern Recognition (CVPR),\n3319–27. IEEE. https://doi.org/10.1109/cvpr.2017.354.\n\n\nBeaton, Albert E., and John W. Tukey. 1974. “The Fitting of Power\nSeries, Meaning Polynomials, Illustrated on Band-Spectroscopic\nData.” Technometrics 16 (2): 147. https://doi.org/10.2307/1267936.\n\n\nBeck, Nathaniel, and Simon Jackman. 1998. “Beyond Linearity by\nDefault: Generalized Additive Models.” Am. J.\nPolit. Sci. 42 (2): 596. https://doi.org/10.2307/2991772.\n\n\nBender, Emily M., and Batya Friedman. 2018. “Data Statements for\nNatural Language Processing: Toward Mitigating System Bias\nand Enabling Better Science.” Transactions of the Association\nfor Computational Linguistics 6 (December): 587–604. https://doi.org/10.1162/tacl_a_00041.\n\n\nBerger, Vance W, and YanYan Zhou. 2014.\n“Kolmogorovsmirnov Test:\nOverview.” Wiley Statsref: Statistics Reference\nOnline.\n\n\nBeyer, Lucas, Olivier J Hénaff, Alexander Kolesnikov, Xiaohua Zhai, and\nAäron van den Oord. 2020. “Are We Done with Imagenet?”\nArXiv Preprint abs/2006.07159. https://arxiv.org/abs/2006.07159.\n\n\nBhagoji, Arjun Nitin, Warren He, Bo Li, and Dawn Song. 2018.\n“Practical Black-Box Attacks on Deep Neural Networks Using\nEfficient Query Mechanisms.” In Proceedings of the European\nConference on Computer Vision (ECCV), 154–69.\n\n\nBhardwaj, Kshitij, Marton Havasi, Yuan Yao, David M. Brooks, José Miguel\nHernández-Lobato, and Gu-Yeon Wei. 2020. “A Comprehensive\nMethodology to Determine Optimal Coherence Interfaces for\nMany-Accelerator SoCs.” In Proceedings of the\nACM/IEEE International Symposium on Low Power Electronics and\nDesign, 145–50. ACM. https://doi.org/10.1145/3370748.3406564.\n\n\nBianco, Simone, Remi Cadene, Luigi Celona, and Paolo Napoletano. 2018.\n“Benchmark Analysis of Representative Deep Neural Network\nArchitectures.” IEEE Access 6: 64270–77.\n\n\nBiega, Asia J., Peter Potash, Hal Daumé, Fernando Diaz, and Michèle\nFinck. 2020. “Operationalizing the Legal Principle of Data\nMinimization for Personalization.” In Proceedings of the 43rd\nInternational ACM SIGIR Conference on Research and Development in\nInformation Retrieval, edited by Jimmy Huang, Yi Chang, Xueqi\nCheng, Jaap Kamps, Vanessa Murdock, Ji-Rong Wen, and Yiqun Liu, 399–408.\nACM. https://doi.org/10.1145/3397271.3401034.\n\n\nBiggio, Battista, Blaine Nelson, and Pavel Laskov. 2012.\n“Poisoning Attacks Against Support Vector Machines.” In\nProceedings of the 29th International Conference on Machine\nLearning, ICML 2012, Edinburgh, Scotland, UK, June 26 - July 1,\n2012. icml.cc / Omnipress. http://icml.cc/2012/papers/880.pdf.\n\n\nBiggs, John, James Myers, Jedrzej Kufel, Emre Ozer, Simon Craske, Antony\nSou, Catherine Ramsdale, Ken Williamson, Richard Price, and Scott White.\n2021. “A Natively Flexible 32-Bit Arm Microprocessor.”\nNature 595 (7868): 532–36. https://doi.org/10.1038/s41586-021-03625-w.\n\n\nBinkert, Nathan, Bradford Beckmann, Gabriel Black, Steven K. Reinhardt,\nAli Saidi, Arkaprava Basu, Joel Hestness, et al. 2011. “The Gem5\nSimulator.” ACM SIGARCH Computer Architecture News 39\n(2): 1–7. https://doi.org/10.1145/2024716.2024718.\n\n\nBohr, Adam, and Kaveh Memarzadeh. 2020. “The Rise of Artificial\nIntelligence in Healthcare Applications.” In Artificial\nIntelligence in Healthcare, 25–60. Elsevier. https://doi.org/10.1016/b978-0-12-818438-7.00002-2.\n\n\nBolchini, Cristiana, Luca Cassano, Antonio Miele, and Alessandro Toschi.\n2023. “Fast and Accurate Error Simulation for CNNs\nAgainst Soft Errors.” IEEE Trans. Comput. 72 (4):\n984–97. https://doi.org/10.1109/tc.2022.3184274.\n\n\nBondi, Elizabeth, Ashish Kapoor, Debadeepta Dey, James Piavis, Shital\nShah, Robert Hannaford, Arvind Iyer, Lucas Joppa, and Milind Tambe.\n2018. “Near Real-Time Detection of Poachers from Drones in\nAirSim.” In Proceedings of the Twenty-Seventh\nInternational Joint Conference on Artificial Intelligence, edited\nby Jérôme Lang, 5814–16. International Joint Conferences on Artificial\nIntelligence Organization. https://doi.org/10.24963/ijcai.2018/847.\n\n\nBourtoule, Lucas, Varun Chandrasekaran, Christopher A. Choquette-Choo,\nHengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas\nPapernot. 2021. “Machine Unlearning.” In 2021 IEEE\nSymposium on Security and Privacy (SP), 141–59. IEEE; IEEE. https://doi.org/10.1109/sp40001.2021.00019.\n\n\nBreier, Jakub, Xiaolu Hou, Dirmanto Jap, Lei Ma, Shivam Bhasin, and Yang\nLiu. 2018. “Deeplaser: Practical Fault Attack on Deep\nNeural Networks.” ArXiv Preprint abs/1806.05859. https://arxiv.org/abs/1806.05859.\n\n\nBrown, Tom B., Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan,\nPrafulla Dhariwal, Arvind Neelakantan, et al. 2020. “Language\nModels Are Few-Shot Learners.” In Advances in Neural\nInformation Processing Systems 33: Annual Conference on Neural\nInformation Processing Systems 2020, NeurIPS 2020, December 6-12, 2020,\nVirtual, edited by Hugo Larochelle, Marc’Aurelio Ranzato, Raia\nHadsell, Maria-Florina Balcan, and Hsuan-Tien Lin. https://proceedings.neurips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html.\n\n\nBuolamwini, Joy, and Timnit Gebru. 2018. “Gender Shades:\nIntersectional Accuracy Disparities in Commercial Gender\nClassification.” In Conference on Fairness, Accountability\nand Transparency, 77–91. PMLR.\n\n\nBurnet, David, and Richard Thomas. 1989. “Spycatcher:\nThe Commodification of Truth.” J. Law Soc.\n16 (2): 210. https://doi.org/10.2307/1410360.\n\n\nBurr, Geoffrey W., Matthew J. BrightSky, Abu Sebastian, Huai-Yu Cheng,\nJau-Yi Wu, Sangbum Kim, Norma E. Sosa, et al. 2016. “Recent\nProgress in Phase-Change?Pub _Newline ?Memory\nTechnology.” IEEE Journal on Emerging and Selected Topics in\nCircuits and Systems 6 (2): 146–62. https://doi.org/10.1109/jetcas.2016.2547718.\n\n\nBushnell, Michael L, and Vishwani D Agrawal. 2002. “Built-in\nSelf-Test.” Essentials of Electronic Testing for Digital,\nMemory and Mixed-Signal VLSI Circuits, 489–548.\n\n\nBuyya, Rajkumar, Anton Beloglazov, and Jemal Abawajy. 2010.\n“Energy-Efficient Management of Data Center Resources for Cloud\nComputing: A Vision, Architectural Elements, and Open\nChallenges.” https://arxiv.org/abs/1006.0308.\n\n\nCai, Carrie J., Emily Reif, Narayan Hegde, Jason Hipp, Been Kim, Daniel\nSmilkov, Martin Wattenberg, et al. 2019. “Human-Centered Tools for\nCoping with Imperfect Algorithms During Medical Decision-Making.”\nIn Proceedings of the 2019 CHI Conference on Human Factors in\nComputing Systems, edited by Jennifer G. Dy and Andreas Krause,\n80:2673–82. Proceedings of Machine Learning Research. ACM. https://doi.org/10.1145/3290605.3300234.\n\n\nCai, Han, Chuang Gan, Ligeng Zhu, and Song Han. 2020.\n“TinyTL: Reduce Memory, Not Parameters\nfor Efficient on-Device Learning.” In Advances in Neural\nInformation Processing Systems 33: Annual Conference on Neural\nInformation Processing Systems 2020, NeurIPS 2020, December 6-12, 2020,\nVirtual, edited by Hugo Larochelle, Marc’Aurelio Ranzato, Raia\nHadsell, Maria-Florina Balcan, and Hsuan-Tien Lin. https://proceedings.neurips.cc/paper/2020/hash/81f7acabd411274fcf65ce2070ed568a-Abstract.html.\n\n\nCai, Han, Ligeng Zhu, and Song Han. 2019.\n“ProxylessNAS: Direct Neural\nArchitecture Search on Target Task and Hardware.” In 7th\nInternational Conference on Learning Representations, ICLR 2019, New\nOrleans, LA, USA, May 6-9, 2019. OpenReview.net. https://openreview.net/forum?id=HylVB3AqYm.\n\n\nCalvo, Rafael A, Dorian Peters, Karina Vold, and Richard M Ryan. 2020.\n“Supporting Human Autonomy in AI Systems:\nA Framework for Ethical Enquiry.” Ethics of\nDigital Well-Being: A Multidisciplinary Approach, 31–54.\n\n\nCarlini, Nicholas, Pratyush Mishra, Tavish Vaidya, Yuankai Zhang, Micah\nSherr, Clay Shields, David Wagner, and Wenchao Zhou. 2016. “Hidden\nVoice Commands.” In 25th USENIX Security Symposium (USENIX\nSecurity 16), 513–30.\n\n\nCarta, Salvatore, Alessandro Sebastian Podda, Diego Reforgiato Recupero,\nand Roberto Saia. 2020. “A Local Feature Engineering Strategy to\nImprove Network Anomaly Detection.” Future Internet 12\n(10): 177. https://doi.org/10.3390/fi12100177.\n\n\nCavoukian, Ann. 2009. “Privacy by Design.” Office of\nthe Information and Privacy Commissioner.\n\n\nCenci, Marcelo Pilotto, Tatiana Scarazzato, Daniel Dotto Munchen, Paula\nCristina Dartora, Hugo Marcelo Veit, Andrea Moura Bernardes, and Pablo\nR. Dias. 2021. “Eco-Friendly\nElectronicsA Comprehensive Review.”\nAdv. Mater. Technol. 7 (2): 2001263. https://doi.org/10.1002/admt.202001263.\n\n\nChallenge, WEF Net-Zero. 2021. “The Supply Chain\nOpportunity.” In World Economic Forum: Geneva,\nSwitzerland.\n\n\nChandola, Varun, Arindam Banerjee, and Vipin Kumar. 2009. “Anomaly\nDetection: A Survey.” ACM Comput. Surv. 41 (3): 1–58. https://doi.org/10.1145/1541880.1541882.\n\n\nChapelle, O., B. Scholkopf, and A. Zien Eds. 2009.\n“Semi-Supervised Learning (Chapelle, O.\nEt Al., Eds.; 2006) [Book Reviews].” IEEE Trans.\nNeural Networks 20 (3): 542–42. https://doi.org/10.1109/tnn.2009.2015974.\n\n\nChen, Chaofan, Oscar Li, Daniel Tao, Alina Barnett, Cynthia Rudin, and\nJonathan Su. 2019. “This Looks Like That: Deep\nLearning for Interpretable Image Recognition.” In Advances in\nNeural Information Processing Systems 32: Annual Conference on Neural\nInformation Processing Systems 2019, NeurIPS 2019, December 8-14, 2019,\nVancouver, BC, Canada, edited by Hanna M. Wallach, Hugo Larochelle,\nAlina Beygelzimer, Florence d’Alché-Buc, Emily B. Fox, and Roman\nGarnett, 8928–39. https://proceedings.neurips.cc/paper/2019/hash/adf7ee2dcf142b0e11888e72b43fcb75-Abstract.html.\n\n\nChen, Emma, Shvetank Prakash, Vijay Janapa Reddi, David Kim, and Pranav\nRajpurkar. 2023. “A Framework for Integrating Artificial\nIntelligence for Clinical Care with Continuous Therapeutic\nMonitoring.” Nat. Biomed. Eng., November. https://doi.org/10.1038/s41551-023-01115-0.\n\n\nChen, H.-W. 2006. “Gallium, Indium, and Arsenic Pollution of\nGroundwater from a Semiconductor Manufacturing Area of\nTaiwan.” B. Environ. Contam. Tox. 77 (2):\n289–96. https://doi.org/10.1007/s00128-006-1062-3.\n\n\nChen, Tianqi, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan,\nHaichen Shen, Meghan Cowan, et al. 2018. “TVM:\nAn Automated End-to-End Optimizing Compiler for Deep\nLearning.” In 13th USENIX Symposium on Operating Systems\nDesign and Implementation (OSDI 18), 578–94.\n\n\nChen, Tianqi, Bing Xu, Chiyuan Zhang, and Carlos Guestrin. 2016.\n“Training Deep Nets with Sublinear Memory Cost.” ArXiv\nPreprint abs/1604.06174. https://arxiv.org/abs/1604.06174.\n\n\nChen, Zhiyong, and Shugong Xu. 2023. “Learning\nDomain-Heterogeneous Speaker Recognition Systems with Personalized\nContinual Federated Learning.” EURASIP Journal on Audio,\nSpeech, and Music Processing 2023 (1): 33. https://doi.org/10.1186/s13636-023-00299-2.\n\n\nChen, Zitao, Guanpeng Li, Karthik Pattabiraman, and Nathan DeBardeleben.\n2019. “iBinFI/i: An Efficient Fault\nInjector for Safety-Critical Machine Learning Systems.” In\nProceedings of the International Conference for High Performance\nComputing, Networking, Storage and Analysis. SC ’19. New York, NY,\nUSA: ACM. https://doi.org/10.1145/3295500.3356177.\n\n\nChen, Zitao, Niranjhana Narayanan, Bo Fang, Guanpeng Li, Karthik\nPattabiraman, and Nathan DeBardeleben. 2020.\n“TensorFI: A Flexible Fault Injection\nFramework for TensorFlow Applications.” In 2020\nIEEE 31st International Symposium on Software Reliability Engineering\n(ISSRE), 426–35. IEEE; IEEE. https://doi.org/10.1109/issre5003.2020.00047.\n\n\nCheng, Eric, Shahrzad Mirkhani, Lukasz G. Szafaryn, Chen-Yong Cher,\nHyungmin Cho, Kevin Skadron, Mircea R. Stan, et al. 2016. “Clear:\nuC/u Ross u-l/u Ayer uE/u Xploration for uA/u Rchitecting uR/u Esilience\n- Combining Hardware and Software Techniques to Tolerate Soft Errors in\nProcessor Cores.” In Proceedings of the 53rd Annual Design\nAutomation Conference, 1–6. ACM. https://doi.org/10.1145/2897937.2897996.\n\n\nCheng, Yu, Duo Wang, Pan Zhou, and Tao Zhang. 2018. “Model\nCompression and Acceleration for Deep Neural Networks: The\nPrinciples, Progress, and Challenges.” IEEE Signal Process\nMag. 35 (1): 126–36. https://doi.org/10.1109/msp.2017.2765695.\n\n\nChi, Ping, Shuangchen Li, Cong Xu, Tao Zhang, Jishen Zhao, Yongpan Liu,\nYu Wang, and Yuan Xie. 2016. “Prime: A Novel Processing-in-Memory\nArchitecture for Neural Network Computation in ReRAM-Based Main\nMemory.” ACM SIGARCH Computer Architecture News 44 (3):\n27–39. https://doi.org/10.1145/3007787.3001140.\n\n\nChollet, François. 2018. “Introduction to Keras.” March\n9th.\n\n\nChristiano, Paul F., Jan Leike, Tom B. Brown, Miljan Martic, Shane Legg,\nand Dario Amodei. 2017. “Deep Reinforcement Learning from Human\nPreferences.” In Advances in Neural Information Processing\nSystems 30: Annual Conference on Neural Information Processing Systems\n2017, December 4-9, 2017, Long Beach, CA, USA, edited by Isabelle\nGuyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S.\nV. N. Vishwanathan, and Roman Garnett, 4299–4307. https://proceedings.neurips.cc/paper/2017/hash/d5e2c0adad503c91f91df240d0cd4e49-Abstract.html.\n\n\nChu, Grace, Okan Arikan, Gabriel Bender, Weijun Wang, Achille Brighton,\nPieter-Jan Kindermans, Hanxiao Liu, Berkin Akin, Suyog Gupta, and Andrew\nHoward. 2021. “Discovering Multi-Hardware Mobile Models via\nArchitecture Search.” In 2021 IEEE/CVF Conference on Computer\nVision and Pattern Recognition Workshops (CVPRW), 3022–31. IEEE. https://doi.org/10.1109/cvprw53098.2021.00337.\n\n\nChua, L. 1971. “Memristor-the Missing Circuit Element.”\n#IEEE_J_CT# 18 (5): 507–19. https://doi.org/10.1109/tct.1971.1083337.\n\n\nChung, Jae-Won, Yile Gu, Insu Jang, Luoxi Meng, Nikhil Bansal, and\nMosharaf Chowdhury. 2023. “Perseus: Removing Energy\nBloat from Large Model Training.” ArXiv Preprint\nabs/2312.06902. https://arxiv.org/abs/2312.06902.\n\n\nCohen, Maxime C., Ruben Lobel, and Georgia Perakis. 2016. “The\nImpact of Demand Uncertainty on Consumer Subsidies for Green Technology\nAdoption.” Manage. Sci. 62 (5): 1235–58. https://doi.org/10.1287/mnsc.2015.2173.\n\n\nColeman, Cody, Edward Chou, Julian Katz-Samuels, Sean Culatana, Peter\nBailis, Alexander C. Berg, Robert D. Nowak, Roshan Sumbaly, Matei\nZaharia, and I. Zeki Yalniz. 2022. “Similarity Search for\nEfficient Active Learning and Search of Rare Concepts.” In\nThirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022,\nThirty-Fourth Conference on Innovative Applications of Artificial\nIntelligence, IAAI 2022, the Twelveth Symposium on Educational Advances\nin Artificial Intelligence, EAAI 2022 Virtual Event, February 22 - March\n1, 2022, 6402–10. AAAI Press. https://ojs.aaai.org/index.php/AAAI/article/view/20591.\n\n\nColeman, Cody, Daniel Kang, Deepak Narayanan, Luigi Nardi, Tian Zhao,\nJian Zhang, Peter Bailis, Kunle Olukotun, Chris Ré, and Matei Zaharia.\n2019. “Analysis of DAWNBench, a Time-to-Accuracy\nMachine Learning Performance Benchmark.” ACM SIGOPS Operating\nSystems Review 53 (1): 14–25. https://doi.org/10.1145/3352020.3352024.\n\n\nConstantinescu, Cristian. 2008. “Intermittent Faults and Effects\non Reliability of Integrated Circuits.” In 2008 Annual\nReliability and Maintainability Symposium, 370–74. IEEE; IEEE. https://doi.org/10.1109/rams.2008.4925824.\n\n\nCooper, Tom, Suzanne Fallender, Joyann Pafumi, Jon Dettling, Sebastien\nHumbert, and Lindsay Lessard. 2011. “A Semiconductor Company’s\nExamination of Its Water Footprint Approach.” In Proceedings\nof the 2011 IEEE International Symposium on Sustainable Systems and\nTechnology, 1–6. IEEE; IEEE. https://doi.org/10.1109/issst.2011.5936865.\n\n\nCope, Gord. 2009. “Pure Water, Semiconductors and the\nRecession.” Global Water Intelligence 10 (10).\n\n\nCourbariaux, Matthieu, Itay Hubara, Daniel Soudry, Ran El-Yaniv, and\nYoshua Bengio. 2016. “Binarized Neural Networks:\nTraining Deep Neural Networks with Weights and Activations\nConstrained to+ 1 or-1.” arXiv Preprint\narXiv:1602.02830.\n\n\nD’ignazio, Catherine, and Lauren F Klein. 2023. Data Feminism.\nMIT press.\n\n\nDarvish Rouhani, Bita, Azalia Mirhoseini, and Farinaz Koushanfar. 2017.\n“TinyDL: Just-in-time\nDeep Learning Solution for Constrained Embedded Systems.” In\n2017 IEEE International Symposium on Circuits and Systems\n(ISCAS), 1–4. IEEE. https://doi.org/10.1109/iscas.2017.8050343.\n\n\nDavarzani, Samaneh, David Saucier, Purva Talegaonkar, Erin Parker, Alana\nTurner, Carver Middleton, Will Carroll, et al. 2023. “Closing the\nWearable Gap: Footankle\nKinematic Modeling via Deep Learning Models Based on a Smart Sock\nWearable.” Wearable Technologies 4. https://doi.org/10.1017/wtc.2023.3.\n\n\nDavid, Robert, Jared Duke, Advait Jain, Vijay Janapa Reddi, Nat\nJeffries, Jian Li, Nick Kreeger, et al. 2021. “Tensorflow Lite\nMicro: Embedded Machine Learning for Tinyml\nSystems.” Proceedings of Machine Learning and Systems 3:\n800–811.\n\n\nDavies, Emma. 2011. “Endangered Elements: Critical\nThinking.” https://www.rsc.org/images/Endangered\\%20Elements\\%20-\\%20Critical\\%20Thinking\\_tcm18-196054.pdf.\n\n\nDavies, Mike, Narayan Srinivasa, Tsung-Han Lin, Gautham Chinya,\nYongqiang Cao, Sri Harsha Choday, Georgios Dimou, et al. 2018.\n“Loihi: A Neuromorphic Manycore Processor with\non-Chip Learning.” IEEE Micro 38 (1): 82–99. https://doi.org/10.1109/mm.2018.112130359.\n\n\nDavies, Mike, Andreas Wild, Garrick Orchard, Yulia Sandamirskaya,\nGabriel A. Fonseca Guerra, Prasad Joshi, Philipp Plank, and Sumedh R.\nRisbud. 2021. “Advancing Neuromorphic Computing with Loihi:\nA Survey of Results and Outlook.” Proc.\nIEEE 109 (5): 911–34. https://doi.org/10.1109/jproc.2021.3067593.\n\n\nDavis, Jacqueline, Daniel Bizo, Andy Lawrence, Owen Rogers, and Max\nSmolaks. 2022. “Uptime Institute Global Data Center Survey\n2022.” Uptime Institute.\n\n\nDayarathna, Miyuru, Yonggang Wen, and Rui Fan. 2016. “Data Center\nEnergy Consumption Modeling: A Survey.” IEEE\nCommunications Surveys &Amp; Tutorials 18 (1): 732–94. https://doi.org/10.1109/comst.2015.2481183.\n\n\nDean, Jeffrey, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Quoc\nV. Le, Mark Z. Mao, et al. 2012. “Large Scale Distributed Deep\nNetworks.” In Advances in Neural Information Processing\nSystems 25: 26th Annual Conference on Neural Information Processing\nSystems 2012. Proceedings of a Meeting Held December 3-6, 2012, Lake\nTahoe, Nevada, United States, edited by Peter L. Bartlett, Fernando\nC. N. Pereira, Christopher J. C. Burges, Léon Bottou, and Kilian Q.\nWeinberger, 1232–40. https://proceedings.neurips.cc/paper/2012/hash/6aca97005c68f1206823815f66102863-Abstract.html.\n\n\nDeng, Jia, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Fei-Fei Li.\n2009. “ImageNet: A Large-Scale\nHierarchical Image Database.” In 2009 IEEE Conference on\nComputer Vision and Pattern Recognition, 248–55. IEEE. https://doi.org/10.1109/cvpr.2009.5206848.\n\n\nDesai, Tanvi, Felix Ritchie, Richard Welpton, et al. 2016. “Five\nSafes: Designing Data Access for Research.”\nEconomics Working Paper Series 1601: 28.\n\n\nDevlin, Jacob, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019.\n“BERT: Pre-training of\nDeep Bidirectional Transformers for Language Understanding.” In\nProceedings of the 2019 Conference of the North, 4171–86.\nMinneapolis, Minnesota: Association for Computational Linguistics. https://doi.org/10.18653/v1/n19-1423.\n\n\nDhar, Sauptik, Junyao Guo, Jiayi (Jason) Liu, Samarth Tripathi, Unmesh\nKurup, and Mohak Shah. 2021. “A Survey of on-Device Machine\nLearning: An Algorithms and Learning Theory Perspective.” ACM\nTransactions on Internet of Things 2 (3): 1–49. https://doi.org/10.1145/3450494.\n\n\nDong, Xin, Barbara De Salvo, Meng Li, Chiao Liu, Zhongnan Qu, H. T.\nKung, and Ziyun Li. 2022. “SplitNets:\nDesigning Neural Architectures for Efficient Distributed\nComputing on Head-Mounted Systems.” In 2022 IEEE/CVF\nConference on Computer Vision and Pattern Recognition (CVPR),\n12549–59. IEEE. https://doi.org/10.1109/cvpr52688.2022.01223.\n\n\nDongarra, Jack J. 2009. “The Evolution of High Performance\nComputing on System z.” IBM J. Res. Dev. 53: 3–4.\n\n\nDuarte, Javier, Nhan Tran, Ben Hawks, Christian Herwig, Jules Muhizi,\nShvetank Prakash, and Vijay Janapa Reddi. 2022.\n“FastML Science Benchmarks: Accelerating\nReal-Time Scientific Edge Machine Learning.” ArXiv\nPreprint abs/2207.07958. https://arxiv.org/abs/2207.07958.\n\n\nDuchi, John C., Elad Hazan, and Yoram Singer. 2010. “Adaptive\nSubgradient Methods for Online Learning and Stochastic\nOptimization.” In COLT 2010 - the 23rd Conference on Learning\nTheory, Haifa, Israel, June 27-29, 2010, edited by Adam Tauman\nKalai and Mehryar Mohri, 257–69. Omnipress. http://colt2010.haifa.il.ibm.com/papers/COLT2010proceedings.pdf#page=265.\n\n\nDuisterhof, Bardienus P, Srivatsan Krishnan, Jonathan J Cruz, Colby R\nBanbury, William Fu, Aleksandra Faust, Guido CHE de Croon, and Vijay\nJanapa Reddi. 2019. “Learning to Seek: Autonomous\nSource Seeking with Deep Reinforcement Learning Onboard a Nano Drone\nMicrocontroller.” ArXiv Preprint abs/1909.11236. https://arxiv.org/abs/1909.11236.\n\n\nDuisterhof, Bardienus P., Shushuai Li, Javier Burgues, Vijay Janapa\nReddi, and Guido C. H. E. de Croon. 2021. “Sniffy Bug:\nA Fully Autonomous Swarm of Gas-Seeking Nano Quadcopters in\nCluttered Environments.” In 2021 IEEE/RSJ International\nConference on Intelligent Robots and Systems (IROS), 9099–9106.\nIEEE; IEEE. https://doi.org/10.1109/iros51168.2021.9636217.\n\n\nDürr, Marc, Gunnar Nissen, Kurt-Wolfram Sühs, Philipp Schwenkenbecher,\nChristian Geis, Marius Ringelstein, Hans-Peter Hartung, et al. 2021.\n“CSF Findings in Acute NMDAR and LGI1 Antibody–Associated\nAutoimmune Encephalitis.” Neurology Neuroimmunology &Amp;\nNeuroinflammation 8 (6). https://doi.org/10.1212/nxi.0000000000001086.\n\n\nDwork, Cynthia, Frank McSherry, Kobbi Nissim, and Adam Smith. 2006.\n“Calibrating Noise to Sensitivity in Private Data\nAnalysis.” In Theory of Cryptography, edited by Shai\nHalevi and Tal Rabin, 265–84. Berlin, Heidelberg: Springer Berlin\nHeidelberg.\n\n\nDwork, Cynthia, and Aaron Roth. 2013. “The Algorithmic Foundations\nof Differential Privacy.” Foundations and Trends\nin Theoretical Computer Science 9 (3-4): 211–407. https://doi.org/10.1561/0400000042.\n\n\nEbrahimi, Khosrow, Gerard F. Jones, and Amy S. Fleischer. 2014. ��A\nReview of Data Center Cooling Technology, Operating Conditions and the\nCorresponding Low-Grade Waste Heat Recovery Opportunities.”\nRenewable Sustainable Energy Rev. 31 (March): 622–38. https://doi.org/10.1016/j.rser.2013.12.007.\n\n\nEgwutuoha, Ifeanyi P., David Levy, Bran Selic, and Shiping Chen. 2013.\n“A Survey of Fault Tolerance Mechanisms and Checkpoint/Restart\nImplementations for High Performance Computing Systems.” The\nJournal of Supercomputing 65 (3): 1302–26. https://doi.org/10.1007/s11227-013-0884-0.\n\n\nEisenman, Assaf, Kiran Kumar Matam, Steven Ingram, Dheevatsa Mudigere,\nRaghuraman Krishnamoorthi, Krishnakumar Nair, Misha Smelyanskiy, and\nMurali Annavaram. 2022. “Check-n-Run: A Checkpointing\nSystem for Training Deep Learning Recommendation Models.” In\n19th USENIX Symposium on Networked Systems Design and Implementation\n(NSDI 22), 929–43.\n\n\nEldan, Ronen, and Mark Russinovich. 2023. “Who’s Harry Potter?\nApproximate Unlearning in LLMs.” ArXiv\nPreprint abs/2310.02238. https://arxiv.org/abs/2310.02238.\n\n\nEl-Rayis, A. O. 2014. “Reconfigurable Architectures for the Next\nGeneration of Mobile Device Telecommunications Systems.” :\nhttps://www.researchgate.net/publication/292608967.\n\n\nEshraghian, Jason K., Max Ward, Emre O. Neftci, Xinxin Wang, Gregor\nLenz, Girish Dwivedi, Mohammed Bennamoun, Doo Seok Jeong, and Wei D. Lu.\n2023. “Training Spiking Neural Networks Using Lessons from Deep\nLearning.” Proc. IEEE 111 (9): 1016–54. https://doi.org/10.1109/jproc.2023.3308088.\n\n\nEsteva, Andre, Brett Kuprel, Roberto A. Novoa, Justin Ko, Susan M.\nSwetter, Helen M. Blau, and Sebastian Thrun. 2017.\n“Dermatologist-Level Classification of Skin Cancer with Deep\nNeural Networks.” Nature 542 (7639): 115–18. https://doi.org/10.1038/nature21056.\n\n\nEykholt, Kevin, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati,\nChaowei Xiao, Atul Prakash, Tadayoshi Kohno, and Dawn Song. 2017.\n“Robust Physical-World Attacks on Deep Learning Models.”\nArXiv Preprint abs/1707.08945. https://arxiv.org/abs/1707.08945.\n\n\nFahim, Farah, Benjamin Hawks, Christian Herwig, James Hirschauer, Sergo\nJindariani, Nhan Tran, Luca P. Carloni, et al. 2021. “Hls4ml:\nAn Open-Source Codesign Workflow to Empower Scientific\nLow-Power Machine Learning Devices.” https://arxiv.org/abs/2103.05579.\n\n\nFarah, Martha J. 2005. “Neuroethics: The Practical\nand the Philosophical.” Trends Cogn. Sci. 9 (1): 34–40.\nhttps://doi.org/10.1016/j.tics.2004.12.001.\n\n\nFarwell, James P., and Rafal Rohozinski. 2011. “Stuxnet and the\nFuture of Cyber War.” Survival 53 (1): 23–40. https://doi.org/10.1080/00396338.2011.555586.\n\n\nFowers, Jeremy, Kalin Ovtcharov, Michael Papamichael, Todd Massengill,\nMing Liu, Daniel Lo, Shlomi Alkalay, et al. 2018. “A Configurable\nCloud-Scale DNN Processor for Real-Time\nAI.” In 2018 ACM/IEEE 45th Annual International\nSymposium on Computer Architecture (ISCA), 1–14. IEEE; IEEE. https://doi.org/10.1109/isca.2018.00012.\n\n\nFrancalanza, Adrian, Luca Aceto, Antonis Achilleos, Duncan Paul Attard,\nIan Cassar, Dario Della Monica, and Anna Ingólfsdóttir. 2017. “A\nFoundation for Runtime Monitoring.” In International\nConference on Runtime Verification, 8–29. Springer.\n\n\nFrankle, Jonathan, and Michael Carbin. 2019. “The Lottery Ticket\nHypothesis: Finding Sparse, Trainable Neural\nNetworks.” In 7th International Conference on Learning\nRepresentations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019.\nOpenReview.net. https://openreview.net/forum?id=rJl-b3RcF7.\n\n\nFriedman, Batya. 1996. “Value-Sensitive Design.”\nInteractions 3 (6): 16–23. https://doi.org/10.1145/242485.242493.\n\n\nFurber, Steve. 2016. “Large-Scale Neuromorphic Computing\nSystems.” J. Neural Eng. 13 (5): 051001. https://doi.org/10.1088/1741-2560/13/5/051001.\n\n\nFursov, Ivan, Matvey Morozov, Nina Kaploukhaya, Elizaveta Kovtun,\nRodrigo Rivera-Castro, Gleb Gusev, Dmitry Babaev, Ivan Kireev, Alexey\nZaytsev, and Evgeny Burnaev. 2021. “Adversarial Attacks on Deep\nModels for Financial Transaction Records.” In Proceedings of\nthe 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data\nMining, 2868–78. ACM. https://doi.org/10.1145/3447548.3467145.\n\n\nGale, Trevor, Erich Elsen, and Sara Hooker. 2019. “The State of\nSparsity in Deep Neural Networks.” ArXiv Preprint\nabs/1902.09574. https://arxiv.org/abs/1902.09574.\n\n\nGandolfi, Karine, Christophe Mourtel, and Francis Olivier. 2001.\n“Electromagnetic Analysis: Concrete Results.”\nIn Cryptographic Hardware and Embedded SystemsCHES\n2001: Third International Workshop Paris, France, May 1416,\n2001 Proceedings 3, 251–61. Springer.\n\n\nGannot, G., and M. Ligthart. 1994. “Verilog HDL Based\nFPGA Design.” In International Verilog HDL\nConference, 86–92. IEEE. https://doi.org/10.1109/ivc.1994.323743.\n\n\nGao, Yansong, Said F. Al-Sarawi, and Derek Abbott. 2020. “Physical\nUnclonable Functions.” Nature Electronics 3 (2): 81–91.\nhttps://doi.org/10.1038/s41928-020-0372-5.\n\n\nGates, Byron D. 2009. “Flexible Electronics.”\nScience 323 (5921): 1566–67. https://doi.org/10.1126/science.1171230.\n\n\nGebru, Timnit, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman\nVaughan, Hanna Wallach, Hal Daumé III, and Kate Crawford. 2021.\n“Datasheets for Datasets.” Commun. ACM 64 (12):\n86–92. https://doi.org/10.1145/3458723.\n\n\nGeiger, Atticus, Hanson Lu, Thomas Icard, and Christopher Potts. 2021.\n“Causal Abstractions of Neural Networks.” In Advances\nin Neural Information Processing Systems 34: Annual Conference on Neural\nInformation Processing Systems 2021, NeurIPS 2021, December 6-14, 2021,\nVirtual, edited by Marc’Aurelio Ranzato, Alina Beygelzimer, Yann N.\nDauphin, Percy Liang, and Jennifer Wortman Vaughan, 9574–86. https://proceedings.neurips.cc/paper/2021/hash/4f5c422f4d49a5a807eda27434231040-Abstract.html.\n\n\nGholami, Dong Kim, Mahoney Yao, and Keutzer. 2021. “A Survey of\nQuantization Methods for Efficient Neural Network Inference).”\nArXiv Preprint. https://arxiv.org/abs/2103.13630.\n\n\nGlorot, Xavier, and Yoshua Bengio. 2010. “Understanding the\nDifficulty of Training Deep Feedforward Neural Networks.” In\nProceedings of the Thirteenth International Conference on Artificial\nIntelligence and Statistics, 249–56. http://proceedings.mlr.press/v9/glorot10a.html.\n\n\nGnad, Dennis R. E., Fabian Oboril, and Mehdi B. Tahoori. 2017.\n“Voltage Drop-Based Fault Attacks on FPGAs Using\nValid Bitstreams.” In 2017 27th International Conference on\nField Programmable Logic and Applications (FPL), 1–7. IEEE; IEEE.\nhttps://doi.org/10.23919/fpl.2017.8056840.\n\n\nGoodfellow, Ian, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David\nWarde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2020.\n“Generative Adversarial Networks.” Commun. ACM 63\n(11): 139–44. https://doi.org/10.1145/3422622.\n\n\nGoodyear, Victoria A. 2017. “Social Media, Apps and Wearable\nTechnologies: Navigating Ethical Dilemmas and\nProcedures.” Qualitative Research in Sport, Exercise and\nHealth 9 (3): 285–302. https://doi.org/10.1080/2159676x.2017.1303790.\n\n\nGoogle. n.d. “Information Quality Content Moderation.” https://blog.google/documents/83/.\n\n\nGordon, Ariel, Elad Eban, Ofir Nachum, Bo Chen, Hao Wu, Tien-Ju Yang,\nand Edward Choi. 2018. “MorphNet: Fast\n&Amp; Simple Resource-Constrained Structure Learning of Deep\nNetworks.” In 2018 IEEE/CVF Conference on Computer Vision and\nPattern Recognition, 1586–95. IEEE. https://doi.org/10.1109/cvpr.2018.00171.\n\n\nGräfe, Ralf, Qutub Syed Sha, Florian Geissler, and Michael Paulitsch.\n2023. “Large-Scale Application of Fault Injection into\nPyTorch Models -an Extension to PyTorchFI for\nValidation Efficiency.” In 2023 53rd Annual IEEE/IFIP\nInternational Conference on Dependable Systems and Networks -\nSupplemental Volume (DSN-s), 56–62. IEEE; IEEE. https://doi.org/10.1109/dsn-s58398.2023.00025.\n\n\nGreengard, Samuel. 2015. The Internet of Things. The MIT Press.\nhttps://doi.org/10.7551/mitpress/10277.001.0001.\n\n\nGrossman, Elizabeth. 2007. High Tech Trash: Digital\nDevices, Hidden Toxics, and Human Health. Island press.\n\n\nGruslys, Audrunas, Rémi Munos, Ivo Danihelka, Marc Lanctot, and Alex\nGraves. 2016. “Memory-Efficient Backpropagation Through\nTime.” In Advances in Neural Information Processing Systems\n29: Annual Conference on Neural Information Processing Systems 2016,\nDecember 5-10, 2016, Barcelona, Spain, edited by Daniel D. Lee,\nMasashi Sugiyama, Ulrike von Luxburg, Isabelle Guyon, and Roman Garnett,\n4125–33. https://proceedings.neurips.cc/paper/2016/hash/a501bebf79d570651ff601788ea9d16d-Abstract.html.\n\n\nGu, Ivy. 2023. “Deep Learning Model Compression (Ii) by Ivy Gu\nMedium.” https://ivygdy.medium.com/deep-learning-model-compression-ii-546352ea9453.\n\n\nGuo, Chuan, Jacob Gardner, Yurong You, Andrew Gordon Wilson, and Kilian\nWeinberger. 2019. “Simple Black-Box Adversarial Attacks.”\nIn International Conference on Machine Learning, 2484–93. PMLR.\n\n\nGuo, Yutao, Hao Wang, Hui Zhang, Tong Liu, Zhaoguang Liang, Yunlong Xia,\nLi Yan, et al. 2019. “Mobile Photoplethysmographic Technology to\nDetect Atrial Fibrillation.” J. Am. Coll. Cardiol. 74\n(19): 2365–75. https://doi.org/10.1016/j.jacc.2019.08.019.\n\n\nGupta, Maanak, Charankumar Akiri, Kshitiz Aryal, Eli Parker, and\nLopamudra Praharaj. 2023. “From ChatGPT to\nThreatGPT: Impact of Generative\nAI in Cybersecurity and Privacy.”\n#IEEE_O_ACC# 11: 80218–45. https://doi.org/10.1109/access.2023.3300381.\n\n\nGupta, Maya, Andrew Cotter, Jan Pfeifer, Konstantin Voevodski, Kevin\nCanini, Alexander Mangylov, Wojciech Moczydlowski, and Alexander Van\nEsbroeck. 2016. “Monotonic Calibrated Interpolated Look-up\nTables.” The Journal of Machine Learning Research 17\n(1): 3790–3836.\n\n\nGupta, Udit, Mariam Elgamal, Gage Hills, Gu-Yeon Wei, Hsien-Hsin S. Lee,\nDavid Brooks, and Carole-Jean Wu. 2022. “Act: Designing\nSustainable Computer Systems with an Architectural Carbon Modeling\nTool.” In Proceedings of the 49th Annual International\nSymposium on Computer Architecture, 784–99. ACM. https://doi.org/10.1145/3470496.3527408.\n\n\nGwennap, Linley. n.d. “Certus-NX Innovates\nGeneral-Purpose FPGAs.”\n\n\nHaensch, Wilfried, Tayfun Gokmen, and Ruchir Puri. 2019. “The Next\nGeneration of Deep Learning Hardware: Analog\nComputing.” Proc. IEEE 107 (1): 108–22. https://doi.org/10.1109/jproc.2018.2871057.\n\n\nHamming, R. W. 1950. “Error Detecting and Error Correcting\nCodes.” Bell Syst. Tech. J. 29 (2): 147–60. https://doi.org/10.1002/j.1538-7305.1950.tb00463.x.\n\n\nHan, Song, Huizi Mao, and William J Dally. 2015. “Deep\nCompression: Compressing Deep Neural Networks with Pruning,\nTrained Quantization and Huffman Coding.” arXiv Preprint\narXiv:1510.00149.\n\n\nHan, Song, Huizi Mao, and William J. Dally. 2016. “Deep\nCompression: Compressing Deep Neural Networks with Pruning,\nTrained Quantization and Huffman Coding.” https://arxiv.org/abs/1510.00149.\n\n\nHandlin, Oscar. 1965. “Science and Technology in Popular\nCulture.” Daedalus-Us., 156–70.\n\n\nHardt, Moritz, Eric Price, and Nati Srebro. 2016. “Equality of\nOpportunity in Supervised Learning.” In Advances in Neural\nInformation Processing Systems 29: Annual Conference on Neural\nInformation Processing Systems 2016, December 5-10, 2016, Barcelona,\nSpain, edited by Daniel D. Lee, Masashi Sugiyama, Ulrike von\nLuxburg, Isabelle Guyon, and Roman Garnett, 3315–23. https://proceedings.neurips.cc/paper/2016/hash/9d2682367c3935defcb1f9e247a97c0d-Abstract.html.\n\n\nHawks, Benjamin, Javier Duarte, Nicholas J. Fraser, Alessandro\nPappalardo, Nhan Tran, and Yaman Umuroglu. 2021. “Ps and Qs: Quantization-aware Pruning for Efficient Low\nLatency Neural Network Inference.” Frontiers in Artificial\nIntelligence 4 (July). https://doi.org/10.3389/frai.2021.676564.\n\n\nHazan, Avi, and Elishai Ezra Tsur. 2021. “Neuromorphic Analog\nImplementation of Neural Engineering Framework-Inspired Spiking Neuron\nfor High-Dimensional Representation.” Front. Neurosci.\n15 (February): 627221. https://doi.org/10.3389/fnins.2021.627221.\n\n\nHe, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2015.\n“Delving Deep into Rectifiers: Surpassing Human-Level Performance\non ImageNet Classification.” In 2015 IEEE International\nConference on Computer Vision (ICCV), 1026–34. IEEE. https://doi.org/10.1109/iccv.2015.123.\n\n\n———. 2016. “Deep Residual Learning for Image Recognition.”\nIn 2016 IEEE Conference on Computer Vision and Pattern Recognition\n(CVPR), 770–78. IEEE. https://doi.org/10.1109/cvpr.2016.90.\n\n\nHe, Yi, Prasanna Balaprakash, and Yanjing Li. 2020.\n“FIdelity: Efficient Resilience Analysis\nFramework for Deep Learning Accelerators.” In 2020 53rd\nAnnual IEEE/ACM International Symposium on Microarchitecture\n(MICRO), 270–81. IEEE; IEEE. https://doi.org/10.1109/micro50266.2020.00033.\n\n\nHe, Yi, Mike Hutton, Steven Chan, Robert De Gruijl, Rama Govindaraju,\nNishant Patil, and Yanjing Li. 2023. “Understanding and Mitigating\nHardware Failures in Deep Learning Training Systems.” In\nProceedings of the 50th Annual International Symposium on Computer\nArchitecture, 1–16. IEEE; ACM. https://doi.org/10.1145/3579371.3589105.\n\n\nHébert-Johnson, Úrsula, Michael P. Kim, Omer Reingold, and Guy N.\nRothblum. 2018. “Multicalibration: Calibration for\nthe (Computationally-Identifiable) Masses.” In\nProceedings of the 35th International Conference on Machine\nLearning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15,\n2018, edited by Jennifer G. Dy and Andreas Krause, 80:1944–53.\nProceedings of Machine Learning Research. PMLR. http://proceedings.mlr.press/v80/hebert-johnson18a.html.\n\n\nHegde, Sumant. 2023. “An Introduction to Separable Convolutions -\nAnalytics Vidhya.” https://www.analyticsvidhya.com/blog/2021/11/an-introduction-to-separable-convolutions/.\n\n\nHenderson, Peter, Jieru Hu, Joshua Romoff, Emma Brunskill, Dan Jurafsky,\nand Joelle Pineau. 2020. “Towards the Systematic Reporting of the\nEnergy and Carbon Footprints of Machine Learning.” The\nJournal of Machine Learning Research 21 (1): 10039–81.\n\n\nHendrycks, Dan, and Thomas Dietterich. 2019. “Benchmarking Neural\nNetwork Robustness to Common Corruptions and Perturbations.”\narXiv Preprint arXiv:1903.12261.\n\n\nHendrycks, Dan, Kevin Zhao, Steven Basart, Jacob Steinhardt, and Dawn\nSong. 2021. “Natural Adversarial Examples.” In 2021\nIEEE/CVF Conference on Computer Vision and Pattern Recognition\n(CVPR), 15262–71. IEEE. https://doi.org/10.1109/cvpr46437.2021.01501.\n\n\nHennessy, John L., and David A. Patterson. 2019. “A New Golden Age\nfor Computer Architecture.” Commun. ACM 62 (2): 48–60.\nhttps://doi.org/10.1145/3282307.\n\n\nHimmelstein, Gracie, David Bates, and Li Zhou. 2022. “Examination\nof Stigmatizing Language in the Electronic Health Record.”\nJAMA Network Open 5 (1): e2144967. https://doi.org/10.1001/jamanetworkopen.2021.44967.\n\n\nHinton, Geoffrey. 2005. “Van Nostrand’s Scientific Encyclopedia.” Wiley.\nhttps://doi.org/10.1002/0471743984.vse0673.\n\n\n———. 2017. “Overview of Minibatch Gradient Descent.”\nUniversity of Toronto; University Lecture.\n\n\nHo Yoon, Jung, Hyung-Suk Jung, Min Hwan Lee, Gun Hwan Kim, Seul Ji Song,\nJun Yeong Seok, Kyung Jean Yoon, et al. 2012. “Frontiers in\nElectronic Materials.” Wiley. https://doi.org/10.1002/9783527667703.ch67.\n\n\nHoefler, Torsten, Dan Alistarh, Tal Ben-Nun, Nikoli Dryden, and\nAlexandra Peste. 2021. “Sparsity in Deep Learning: Pruning and\nGrowth for Efficient Inference and Training in Neural Networks,”\nJanuary. http://arxiv.org/abs/2102.00554v1.\n\n\nHolland, Sarah, Ahmed Hosny, Sarah Newman, Joshua Joseph, and Kasia\nChmielinski. 2020. “The Dataset Nutrition Label: A Framework to\nDrive Higher Data Quality Standards.” In Data Protection and\nPrivacy. Hart Publishing. https://doi.org/10.5040/9781509932771.ch-001.\n\n\nHong, Sanghyun, Nicholas Carlini, and Alexey Kurakin. 2023.\n“Publishing Efficient on-Device Models Increases Adversarial\nVulnerability.” In 2023 IEEE Conference on Secure and\nTrustworthy Machine Learning (SaTML), 271–90. IEEE; IEEE. https://doi.org/10.1109/satml54575.2023.00026.\n\n\nHosseini, Hossein, Sreeram Kannan, Baosen Zhang, and Radha Poovendran.\n2017. “Deceiving Google’s Perspective Api Built for Detecting\nToxic Comments.” ArXiv Preprint abs/1702.08138. https://arxiv.org/abs/1702.08138.\n\n\nHoward, Andrew G., Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun\nWang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. 2017.\n“MobileNets: Efficient Convolutional\nNeural Networks for Mobile Vision Applications.” ArXiv\nPreprint. https://arxiv.org/abs/1704.04861.\n\n\nHsiao, Yu-Shun, Zishen Wan, Tianyu Jia, Radhika Ghosal, Abdulrahman\nMahmoud, Arijit Raychowdhury, David Brooks, Gu-Yeon Wei, and Vijay\nJanapa Reddi. 2023. “MAVFI: An\nEnd-to-End Fault Analysis Framework with Anomaly Detection and Recovery\nfor Micro Aerial Vehicles.” In 2023 Design, Automation\n&Amp; Test in Europe Conference &Amp; Exhibition (DATE),\n1–6. IEEE; IEEE. https://doi.org/10.23919/date56975.2023.10137246.\n\n\nHsu, Liang-Ching, Ching-Yi Huang, Yen-Hsun Chuang, Ho-Wen Chen, Ya-Ting\nChan, Heng Yi Teah, Tsan-Yao Chen, Chiung-Fen Chang, Yu-Ting Liu, and\nYu-Min Tzou. 2016. “Accumulation of Heavy Metals and Trace\nElements in Fluvial Sediments Received Effluents from Traditional and\nSemiconductor Industries.” Scientific Reports 6 (1):\n34250. https://doi.org/10.1038/srep34250.\n\n\nHu, Jie, Li Shen, and Gang Sun. 2018. “Squeeze-and-Excitation\nNetworks.” In 2018 IEEE/CVF Conference on Computer Vision and\nPattern Recognition, 7132–41. IEEE. https://doi.org/10.1109/cvpr.2018.00745.\n\n\nHu, Yang, Jie Jiang, Lifu Zhang, Yunfeng Shi, and Jian Shi. 2023.\n“Halide Perovskite Semiconductors.” Wiley. https://doi.org/10.1002/9783527829026.ch13.\n\n\nHuang, Tsung-Ching, Kenjiro Fukuda, Chun-Ming Lo, Yung-Hui Yeh, Tsuyoshi\nSekitani, Takao Someya, and Kwang-Ting Cheng. 2011.\n“Pseudo-CMOS: A Design Style for\nLow-Cost and Robust Flexible Electronics.” IEEE Trans.\nElectron Devices 58 (1): 141–50. https://doi.org/10.1109/ted.2010.2088127.\n\n\nHutter, Michael, Jorn-Marc Schmidt, and Thomas Plos. 2009.\n“Contact-Based Fault Injections and Power Analysis on\nRFID Tags.” In 2009 European Conference on\nCircuit Theory and Design, 409–12. IEEE; IEEE. https://doi.org/10.1109/ecctd.2009.5275012.\n\n\nIandola, Forrest N, Song Han, Matthew W Moskewicz, Khalid Ashraf,\nWilliam J Dally, and Kurt Keutzer. 2016. “SqueezeNet:\nAlexnet-level Accuracy with 50x Fewer\nParameters and 0.5 MB Model Size.” ArXiv\nPreprint abs/1602.07360. https://arxiv.org/abs/1602.07360.\n\n\nIgnatov, Andrey, Radu Timofte, William Chou, Ke Wang, Max Wu, Tim\nHartley, and Luc Van Gool. 2018. “AI Benchmark:\nRunning Deep Neural Networks on Android\nSmartphones,” 0–0.\n\n\nImani, Mohsen, Abbas Rahimi, and Tajana S. Rosing. 2016.\n“Resistive Configurable Associative Memory for Approximate\nComputing.” In Proceedings of the 2016 Design, Automation\n&Amp; Test in Europe Conference &Amp; Exhibition (DATE),\n1327–32. IEEE; Research Publishing Services. https://doi.org/10.3850/9783981537079_0454.\n\n\nIntelLabs. 2023. “Knowledge Distillation - Neural Network\nDistiller.” https://intellabs.github.io/distiller/knowledge_distillation.html.\n\n\nIppolito, Daphne, Florian Tramer, Milad Nasr, Chiyuan Zhang, Matthew\nJagielski, Katherine Lee, Christopher Choquette Choo, and Nicholas\nCarlini. 2023. “Preventing Generation of Verbatim Memorization in\nLanguage Models Gives a False Sense of Privacy.” In\nProceedings of the 16th International Natural Language Generation\nConference, 5253–70. Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.inlg-main.3.\n\n\nIrimia-Vladu, Mihai. 2014.\n““Green” Electronics:\nBiodegradable and Biocompatible Materials and Devices for\nSustainable Future.” Chem. Soc. Rev. 43 (2): 588–610. https://doi.org/10.1039/c3cs60235d.\n\n\nIsscc. 2014. “Computing’s Energy Problem (and What We Can Do about\nIt).” https://ieeexplore.ieee.org/document/6757323.\n\n\nJacob, Benoit, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang,\nAndrew Howard, Hartwig Adam, and Dmitry Kalenichenko. 2018.\n“Quantization and Training of Neural Networks for Efficient\nInteger-Arithmetic-Only Inference.” In Proceedings of the\nIEEE Conference on Computer Vision and Pattern Recognition,\n2704–13.\n\n\nJaderberg, Max, Valentin Dalibard, Simon Osindero, Wojciech M.\nCzarnecki, Jeff Donahue, Ali Razavi, Oriol Vinyals, et al. 2017.\n“Population Based Training of Neural Networks.” arXiv\nPreprint arXiv:1711.09846, November. http://arxiv.org/abs/1711.09846v2.\n\n\nJanapa Reddi, Vijay, Alexander Elium, Shawn Hymel, David Tischler,\nDaniel Situnayake, Carl Ward, Louis Moreau, et al. 2023. “Edge\nImpulse: An MLOps Platform for Tiny Machine\nLearning.” Proceedings of Machine Learning and Systems\n5.\n\n\nJha, A. R. 2014. Rare Earth Materials: Properties and\nApplications. CRC Press. https://doi.org/10.1201/b17045.\n\n\nJha, Saurabh, Subho Banerjee, Timothy Tsai, Siva K. S. Hari, Michael B.\nSullivan, Zbigniew T. Kalbarczyk, Stephen W. Keckler, and Ravishankar K.\nIyer. 2019. “ML-Based Fault Injection for Autonomous\nVehicles: A Case for Bayesian Fault\nInjection.” In 2019 49th Annual IEEE/IFIP International\nConference on Dependable Systems and Networks (DSN), 112–24. IEEE;\nIEEE. https://doi.org/10.1109/dsn.2019.00025.\n\n\nJia, Yangqing, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan\nLong, Ross Girshick, Sergio Guadarrama, and Trevor Darrell. 2014.\n“Caffe: Convolutional Architecture for Fast Feature\nEmbedding.” In Proceedings of the 22nd ACM International\nConference on Multimedia, 675–78. ACM. https://doi.org/10.1145/2647868.2654889.\n\n\nJia, Zhe, Marco Maggioni, Benjamin Staiger, and Daniele P. Scarpazza.\n2018. “Dissecting the NVIDIA Volta\nGPU Architecture via Microbenchmarking.” ArXiv\nPreprint. https://arxiv.org/abs/1804.06826.\n\n\nJia, Zhenge, Dawei Li, Xiaowei Xu, Na Li, Feng Hong, Lichuan Ping, and\nYiyu Shi. 2023. “Life-Threatening Ventricular Arrhythmia Detection\nChallenge in Implantable\nCardioverterdefibrillators.” Nature Machine\nIntelligence 5 (5): 554–55. https://doi.org/10.1038/s42256-023-00659-9.\n\n\nJia, Zhihao, Matei Zaharia, and Alex Aiken. 2019. “Beyond Data and\nModel Parallelism for Deep Neural Networks.” In Proceedings\nof Machine Learning and Systems 2019, MLSys 2019, Stanford, CA, USA,\nMarch 31 - April 2, 2019, edited by Ameet Talwalkar, Virginia\nSmith, and Matei Zaharia. mlsys.org. https://proceedings.mlsys.org/book/265.pdf.\n\n\nJin, Yilun, Xiguang Wei, Yang Liu, and Qiang Yang. 2020. “Towards\nUtilizing Unlabeled Data in Federated Learning: A Survey\nand Prospective.” arXiv Preprint arXiv:2002.11545.\n\n\nJohnson-Roberson, Matthew, Charles Barto, Rounak Mehta, Sharath Nittur\nSridhar, Karl Rosaen, and Ram Vasudevan. 2017. “Driving in the\nMatrix: Can Virtual Worlds Replace Human-Generated\nAnnotations for Real World Tasks?” In 2017 IEEE International\nConference on Robotics and Automation (ICRA), 746–53. Singapore,\nSingapore: IEEE. https://doi.org/10.1109/icra.2017.7989092.\n\n\nJouppi, Norman P., Cliff Young, Nishant Patil, David Patterson, Gaurav\nAgrawal, Raminder Bajwa, Sarah Bates, et al. 2017a. “In-Datacenter\nPerformance Analysis of a Tensor Processing Unit.” In\nProceedings of the 44th Annual International Symposium on Computer\nArchitecture, 1–12. ISCA ’17. New York, NY, USA: ACM. https://doi.org/10.1145/3079856.3080246.\n\n\n———, et al. 2017b. “In-Datacenter Performance Analysis of a Tensor\nProcessing Unit.” In Proceedings of the 44th Annual\nInternational Symposium on Computer Architecture, 1–12. ISCA ’17.\nNew York, NY, USA: ACM. https://doi.org/10.1145/3079856.3080246.\n\n\nJouppi, Norm, George Kurian, Sheng Li, Peter Ma, Rahul Nagarajan, Lifeng\nNai, Nishant Patil, et al. 2023. “TPU V4:\nAn Optically Reconfigurable Supercomputer for Machine\nLearning with Hardware Support for Embeddings.” In\nProceedings of the 50th Annual International Symposium on Computer\nArchitecture. ISCA ’23. New York, NY, USA: ACM. https://doi.org/10.1145/3579371.3589350.\n\n\nJoye, Marc, and Michael Tunstall. 2012. Fault Analysis in\nCryptography. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-29656-7.\n\n\nKairouz, Peter, Sewoong Oh, and Pramod Viswanath. 2015. “Secure\nMulti-Party Differential Privacy.” In Advances in Neural\nInformation Processing Systems 28: Annual Conference on Neural\nInformation Processing Systems 2015, December 7-12, 2015, Montreal,\nQuebec, Canada, edited by Corinna Cortes, Neil D. Lawrence, Daniel\nD. Lee, Masashi Sugiyama, and Roman Garnett, 2008–16. https://proceedings.neurips.cc/paper/2015/hash/a01610228fe998f515a72dd730294d87-Abstract.html.\n\n\nKalamkar, Dhiraj, Dheevatsa Mudigere, Naveen Mellempudi, Dipankar Das,\nKunal Banerjee, Sasikanth Avancha, Dharma Teja Vooturi, et al. 2019.\n“A Study of BFLOAT16 for Deep Learning\nTraining.” https://arxiv.org/abs/1905.12322.\n\n\nKao, Sheng-Chun, Geonhwa Jeong, and Tushar Krishna. 2020.\n“ConfuciuX: Autonomous Hardware Resource\nAssignment for DNN Accelerators Using Reinforcement\nLearning.” In 2020 53rd Annual IEEE/ACM International\nSymposium on Microarchitecture (MICRO), 622–36. IEEE; IEEE. https://doi.org/10.1109/micro50266.2020.00058.\n\n\nKao, Sheng-Chun, and Tushar Krishna. 2020. “Gamma: Automating the\nHW Mapping of DNN Models on Accelerators via Genetic Algorithm.”\nIn Proceedings of the 39th International Conference on\nComputer-Aided Design, 1–9. ACM. https://doi.org/10.1145/3400302.3415639.\n\n\nKaplan, Jared, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin\nChess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario\nAmodei. 2020. “Scaling Laws for Neural Language Models.”\nArXiv Preprint abs/2001.08361. https://arxiv.org/abs/2001.08361.\n\n\nKarargyris, Alexandros, Renato Umeton, Micah J Sheller, Alejandro\nAristizabal, Johnu George, Anna Wuest, Sarthak Pati, et al. 2023.\n“Federated Benchmarking of Medical Artificial Intelligence with\nMedPerf.” Nature Machine Intelligence 5\n(7): 799–810. https://doi.org/10.1038/s42256-023-00652-2.\n\n\nKaur, Harmanpreet, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna\nWallach, and Jennifer Wortman Vaughan. 2020. “Interpreting\nInterpretability: Understanding Data Scientists’ Use of\nInterpretability Tools for Machine Learning.” In Proceedings\nof the 2020 CHI Conference on Human Factors in Computing Systems,\nedited by Regina Bernhaupt, Florian ’Floyd’Mueller, David Verweij, Josh\nAndres, Joanna McGrenere, Andy Cockburn, Ignacio Avellino, et al., 1–14.\nACM. https://doi.org/10.1145/3313831.3376219.\n\n\nKawazoe Aguilera, Marcos, Wei Chen, and Sam Toueg. 1997.\n“Heartbeat: A Timeout-Free Failure Detector for\nQuiescent Reliable Communication.” In Distributed Algorithms:\n11th International Workshop, WDAG’97 Saarbrücken, Germany, September\n2426, 1997 Proceedings 11, 126–40. Springer.\n\n\nKhan, Mohammad Emtiyaz, and Siddharth Swaroop. 2021.\n“Knowledge-Adaptation Priors.” In Advances in Neural\nInformation Processing Systems 34: Annual Conference on Neural\nInformation Processing Systems 2021, NeurIPS 2021, December 6-14, 2021,\nVirtual, edited by Marc’Aurelio Ranzato, Alina Beygelzimer, Yann N.\nDauphin, Percy Liang, and Jennifer Wortman Vaughan, 19757–70. https://proceedings.neurips.cc/paper/2021/hash/a4380923dd651c195b1631af7c829187-Abstract.html.\n\n\nKiela, Douwe, Max Bartolo, Yixin Nie, Divyansh Kaushik, Atticus Geiger,\nZhengxuan Wu, Bertie Vidgen, et al. 2021. “Dynabench:\nRethinking Benchmarking in NLP.” In\nProceedings of the 2021 Conference of the North American Chapter of\nthe Association for Computational Linguistics: Human Language\nTechnologies, 4110–24. Online: Association for Computational\nLinguistics. https://doi.org/10.18653/v1/2021.naacl-main.324.\n\n\nKim, Jungrae, Michael Sullivan, and Mattan Erez. 2015. “Bamboo\nECC: Strong, Safe, and Flexible Codes for\nReliable Computer Memory.” In 2015 IEEE 21st International\nSymposium on High Performance Computer Architecture (HPCA), 101–12.\nIEEE; IEEE. https://doi.org/10.1109/hpca.2015.7056025.\n\n\nKim, Sunju, Chungsik Yoon, Seunghon Ham, Jihoon Park, Ohun Kwon, Donguk\nPark, Sangjun Choi, Seungwon Kim, Kwonchul Ha, and Won Kim. 2018.\n“Chemical Use in the Semiconductor Manufacturing Industry.”\nInt. J. Occup. Env. Heal. 24 (3-4): 109–18. https://doi.org/10.1080/10773525.2018.1519957.\n\n\nKingma, Diederik P., and Jimmy Ba. 2014. “Adam: A Method for\nStochastic Optimization.” Edited by Yoshua Bengio and Yann LeCun,\nDecember. http://arxiv.org/abs/1412.6980v9.\n\n\nKirkpatrick, James, Razvan Pascanu, Neil Rabinowitz, Joel Veness,\nGuillaume Desjardins, Andrei A. Rusu, Kieran Milan, et al. 2017.\n“Overcoming Catastrophic Forgetting in Neural Networks.”\nProc. Natl. Acad. Sci. 114 (13): 3521–26. https://doi.org/10.1073/pnas.1611835114.\n\n\nKo, Yohan. 2021. “Characterizing System-Level Masking Effects\nAgainst Soft Errors.” Electronics 10 (18): 2286. https://doi.org/10.3390/electronics10182286.\n\n\nKocher, Paul, Jann Horn, Anders Fogh, Daniel Genkin, Daniel Gruss,\nWerner Haas, Mike Hamburg, et al. 2019a. “Spectre Attacks:\nExploiting Speculative Execution.” In 2019 IEEE\nSymposium on Security and Privacy (SP). IEEE. https://doi.org/10.1109/sp.2019.00002.\n\n\n———, et al. 2019b. “Spectre Attacks: Exploiting\nSpeculative Execution.” In 2019 IEEE Symposium on Security\nand Privacy (SP). IEEE. https://doi.org/10.1109/sp.2019.00002.\n\n\nKocher, Paul, Joshua Jaffe, and Benjamin Jun. 1999. “Differential\nPower Analysis.” In Advances in\nCryptologyCRYPTO’99: 19th Annual International Cryptology\nConference Santa Barbara, California, USA, August 1519,\n1999 Proceedings 19, 388–97. Springer.\n\n\nKocher, Paul, Joshua Jaffe, Benjamin Jun, and Pankaj Rohatgi. 2011.\n“Introduction to Differential Power Analysis.” Journal\nof Cryptographic Engineering 1 (1): 5–27. https://doi.org/10.1007/s13389-011-0006-y.\n\n\nKoh, Pang Wei, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma\nPierson, Been Kim, and Percy Liang. 2020. “Concept Bottleneck\nModels.” In Proceedings of the 37th International Conference\non Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event,\n119:5338–48. Proceedings of Machine Learning Research. PMLR. http://proceedings.mlr.press/v119/koh20a.html.\n\n\nKoh, Pang Wei, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin\nZhang, Akshay Balsubramani, Weihua Hu, et al. 2021.\n“WILDS: A Benchmark of in-the-Wild\nDistribution Shifts.” In Proceedings of the 38th\nInternational Conference on Machine Learning, ICML 2021, 18-24 July\n2021, Virtual Event, edited by Marina Meila and Tong Zhang,\n139:5637–64. Proceedings of Machine Learning Research. PMLR. http://proceedings.mlr.press/v139/koh21a.html.\n\n\nKoren, Yehuda, Robert Bell, and Chris Volinsky. 2009. “Matrix\nFactorization Techniques for Recommender Systems.”\nComputer 42 (8): 30–37. https://doi.org/10.1109/mc.2009.263.\n\n\nKrishna, Adithya, Srikanth Rohit Nudurupati, Chandana D G, Pritesh\nDwivedi, André van Schaik, Mahesh Mehendale, and Chetan Singh Thakur.\n2023. “RAMAN: A Re-Configurable and\nSparse TinyML Accelerator for Inference on Edge.” https://arxiv.org/abs/2306.06493.\n\n\nKrishnamoorthi. 2018. “Quantizing Deep Convolutional Networks for\nEfficient Inference: A Whitepaper.” ArXiv\nPreprint. https://arxiv.org/abs/1806.08342.\n\n\nKrishnan, Rayan, Pranav Rajpurkar, and Eric J. Topol. 2022.\n“Self-Supervised Learning in Medicine and Healthcare.”\nNat. Biomed. Eng. 6 (12): 1346–52. https://doi.org/10.1038/s41551-022-00914-1.\n\n\nKrishnan, Srivatsan, Natasha Jaques, Shayegan Omidshafiei, Dan Zhang,\nIzzeddin Gur, Vijay Janapa Reddi, and Aleksandra Faust. 2022.\n“Multi-Agent Reinforcement Learning for Microprocessor Design\nSpace Exploration.” https://arxiv.org/abs/2211.16385.\n\n\nKrishnan, Srivatsan, Amir Yazdanbakhsh, Shvetank Prakash, Jason Jabbour,\nIkechukwu Uchendu, Susobhan Ghosh, Behzad Boroujerdian, et al. 2023.\n“ArchGym: An Open-Source Gymnasium for\nMachine Learning Assisted Architecture Design.” In\nProceedings of the 50th Annual International Symposium on Computer\nArchitecture, 1–16. ACM. https://doi.org/10.1145/3579371.3589049.\n\n\nKrizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. 2012.\n“ImageNet Classification with Deep Convolutional\nNeural Networks.” In Advances in Neural Information\nProcessing Systems 25: 26th Annual Conference on Neural Information\nProcessing Systems 2012. Proceedings of a Meeting Held December 3-6,\n2012, Lake Tahoe, Nevada, United States, edited by Peter L.\nBartlett, Fernando C. N. Pereira, Christopher J. C. Burges, Léon Bottou,\nand Kilian Q. Weinberger, 1106–14. https://proceedings.neurips.cc/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html.\n\n\n———. 2017. “ImageNet Classification with Deep\nConvolutional Neural Networks.” Edited by F. Pereira, C. J.\nBurges, L. Bottou, and K. Q. Weinberger. Commun. ACM 60 (6):\n84–90. https://doi.org/10.1145/3065386.\n\n\nKung, Hsiang Tsung, and Charles E Leiserson. 1979. “Systolic\nArrays (for VLSI).” In Sparse Matrix Proceedings\n1978, 1:256–82. Society for industrial; applied mathematics\nPhiladelphia, PA, USA.\n\n\nKurth, Thorsten, Shashank Subramanian, Peter Harrington, Jaideep Pathak,\nMorteza Mardani, David Hall, Andrea Miele, Karthik Kashinath, and Anima\nAnandkumar. 2023. “FourCastNet:\nAccelerating Global High-Resolution Weather Forecasting\nUsing Adaptive Fourier Neural Operators.” In\nProceedings of the Platform for Advanced Scientific Computing\nConference, 1–11. ACM. https://doi.org/10.1145/3592979.3593412.\n\n\nKuzmin, Andrey, Mart Van Baalen, Yuwei Ren, Markus Nagel, Jorn Peters,\nand Tijmen Blankevoort. 2022. “FP8 Quantization:\nThe Power of the Exponent.” https://arxiv.org/abs/2208.09225.\n\n\nKuznetsova, Alina, Hassan Rom, Neil Alldrin, Jasper Uijlings, Ivan\nKrasin, Jordi Pont-Tuset, Shahab Kamali, et al. 2020. “The Open\nImages Dataset V4: Unified Image Classification, Object\nDetection, and Visual Relationship Detection at Scale.”\nInternational Journal of Computer Vision 128 (7): 1956–81.\n\n\nKwon, Jisu, and Daejin Park. 2021. “Hardware/Software\nCo-Design for TinyML Voice-Recognition Application on\nResource Frugal Edge Devices.” Applied Sciences 11 (22):\n11073. https://doi.org/10.3390/app112211073.\n\n\nKwon, Sun Hwa, and Lin Dong. 2022. “Flexible Sensors and Machine\nLearning for Heart Monitoring.” Nano Energy 102\n(November): 107632. https://doi.org/10.1016/j.nanoen.2022.107632.\n\n\nKwon, Young D, Rui Li, Stylianos I Venieris, Jagmohan Chauhan, Nicholas\nD Lane, and Cecilia Mascolo. 2023. “TinyTrain:\nDeep Neural Network Training at the Extreme Edge.”\nArXiv Preprint abs/2307.09988. https://arxiv.org/abs/2307.09988.\n\n\nLai, Liangzhen, Naveen Suda, and Vikas Chandra. 2018a. “Cmsis-Nn:\nEfficient Neural Network Kernels for Arm Cortex-m\nCpus.” ArXiv Preprint abs/1801.06601. https://arxiv.org/abs/1801.06601.\n\n\n———. 2018b. “CMSIS-NN:\nEfficient Neural Network Kernels for Arm Cortex-m\nCPUs.” https://arxiv.org/abs/1801.06601.\n\n\nLakkaraju, Himabindu, and Osbert Bastani. 2020.\n“”How Do i Fool You?”:\nManipulating User Trust via Misleading Black Box Explanations.”\nIn Proceedings of the AAAI/ACM Conference on AI, Ethics, and\nSociety, 79–85. ACM. https://doi.org/10.1145/3375627.3375833.\n\n\nLam, Remi, Alvaro Sanchez-Gonzalez, Matthew Willson, Peter Wirnsberger,\nMeire Fortunato, Ferran Alet, Suman Ravuri, et al. 2023. “Learning\nSkillful Medium-Range Global Weather Forecasting.”\nScience 382 (6677): 1416–21. https://doi.org/10.1126/science.adi2336.\n\n\nLannelongue, Loı̈c, Jason Grealey, and Michael Inouye. 2021. “Green\nAlgorithms: Quantifying the Carbon Footprint of\nComputation.” Adv. Sci. 8 (12): 2100707. https://doi.org/10.1002/advs.202100707.\n\n\nLeCun, Yann, John Denker, and Sara Solla. 1989. “Optimal Brain\nDamage.” Adv Neural Inf Process Syst 2.\n\n\nLee, Minwoong, Namho Lee, Huijeong Gwon, Jongyeol Kim, Younggwan Hwang,\nand Seongik Cho. 2022. “Design of Radiation-Tolerant High-Speed\nSignal Processing Circuit for Detecting Prompt Gamma Rays by Nuclear\nExplosion.” Electronics 11 (18): 2970. https://doi.org/10.3390/electronics11182970.\n\n\nLeRoy Poff, N, MM Brinson, and JW Day. 2002. “Aquatic Ecosystems\n& Global Climate Change.” Pew Center on Global Climate\nChange.\n\n\nLi, En, Liekang Zeng, Zhi Zhou, and Xu Chen. 2020. “Edge\nAI: On-demand Accelerating Deep\nNeural Network Inference via Edge Computing.” IEEE Trans.\nWireless Commun. 19 (1): 447–57. https://doi.org/10.1109/twc.2019.2946140.\n\n\nLi, Guanpeng, Siva Kumar Sastry Hari, Michael Sullivan, Timothy Tsai,\nKarthik Pattabiraman, Joel Emer, and Stephen W. Keckler. 2017.\n“Understanding Error Propagation in Deep Learning Neural Network\n(DNN) Accelerators and Applications.” In\nProceedings of the International Conference for High Performance\nComputing, Networking, Storage and Analysis, 1–12. ACM. https://doi.org/10.1145/3126908.3126964.\n\n\nLi, Jingzhen, Igbe Tobore, Yuhang Liu, Abhishek Kandwal, Lei Wang, and\nZedong Nie. 2021. “Non-Invasive Monitoring of Three Glucose Ranges\nBased on ECG by Using\nDBSCAN-CNN.” #IEEE_J_BHI# 25\n(9): 3340–50. https://doi.org/10.1109/jbhi.2021.3072628.\n\n\nLi, Mu, David G. Andersen, Alexander J. Smola, and Kai Yu. 2014.\n“Communication Efficient Distributed Machine Learning with the\nParameter Server.” In Advances in Neural Information\nProcessing Systems 27: Annual Conference on Neural Information\nProcessing Systems 2014, December 8-13 2014, Montreal, Quebec,\nCanada, edited by Zoubin Ghahramani, Max Welling, Corinna Cortes,\nNeil D. Lawrence, and Kilian Q. Weinberger, 19–27. https://proceedings.neurips.cc/paper/2014/hash/1ff1de774005f8da13f42943881c655f-Abstract.html.\n\n\nLi, Qinbin, Zeyi Wen, Zhaomin Wu, Sixu Hu, Naibo Wang, Yuan Li, Xu Liu,\nand Bingsheng He. 2023. “A Survey on Federated Learning Systems:\nVision, Hype and Reality for Data Privacy and\nProtection.” IEEE Trans. Knowl. Data Eng. 35 (4):\n3347–66. https://doi.org/10.1109/tkde.2021.3124599.\n\n\nLi, Tian, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith. 2020.\n“Federated Learning: Challenges, Methods, and Future\nDirections.” IEEE Signal Process Mag. 37 (3): 50–60. https://doi.org/10.1109/msp.2020.2975749.\n\n\nLi, Xiang, Tao Qin, Jian Yang, and Tie-Yan Liu. 2016.\n“LightRNN: Memory and\nComputation-Efficient Recurrent Neural Networks.” In Advances\nin Neural Information Processing Systems 29: Annual Conference on Neural\nInformation Processing Systems 2016, December 5-10, 2016, Barcelona,\nSpain, edited by Daniel D. Lee, Masashi Sugiyama, Ulrike von\nLuxburg, Isabelle Guyon, and Roman Garnett, 4385–93. https://proceedings.neurips.cc/paper/2016/hash/c3e4035af2a1cde9f21e1ae1951ac80b-Abstract.html.\n\n\nLi, Yuhang, Xin Dong, and Wei Wang. 2020. “Additive Powers-of-Two\nQuantization: An Efficient Non-Uniform Discretization for\nNeural Networks.” In 8th International Conference on Learning\nRepresentations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30,\n2020. OpenReview.net. https://openreview.net/forum?id=BkgXT24tDS.\n\n\nLi, Zhizhong, and Derek Hoiem. 2018. “Learning Without\nForgetting.” IEEE Trans. Pattern Anal. Mach. Intell. 40\n(12): 2935–47. https://doi.org/10.1109/tpami.2017.2773081.\n\n\nLin, Ji, Wei-Ming Chen, Yujun Lin, John Cohn, Chuang Gan, and Song Han.\n2020. “MCUNet: Tiny Deep Learning on\nIoT Devices.” In Advances in Neural Information\nProcessing Systems 33: Annual Conference on Neural Information\nProcessing Systems 2020, NeurIPS 2020, December 6-12, 2020,\nVirtual, edited by Hugo Larochelle, Marc’Aurelio Ranzato, Raia\nHadsell, Maria-Florina Balcan, and Hsuan-Tien Lin. https://proceedings.neurips.cc/paper/2020/hash/86c51678350f656dcc7f490a43946ee5-Abstract.html.\n\n\nLin, Ji, Ligeng Zhu, Wei-Ming Chen, Wei-Chen Wang, Chuang Gan, and Song\nHan. 2022. “On-Device Training Under 256kb Memory.”\nAdv. Neur. In. 35: 22941–54.\n\n\nLin, Ji, Ligeng Zhu, Wei-Ming Chen, Wei-Chen Wang, and Song Han. 2023.\n“Tiny Machine Learning: Progress and Futures Feature.”\nIEEE Circuits Syst. Mag. 23 (3): 8–34. https://doi.org/10.1109/mcas.2023.3302182.\n\n\nLin, Tsung-Yi, Michael Maire, Serge Belongie, James Hays, Pietro Perona,\nDeva Ramanan, Piotr Dollár, and C Lawrence Zitnick. 2014.\n“Microsoft Coco: Common Objects in Context.”\nIn Computer VisionECCV 2014: 13th European Conference,\nZurich, Switzerland, September 6-12, 2014, Proceedings, Part v 13,\n740–55. Springer.\n\n\nLindgren, Simon. 2023. Handbook of Critical Studies of Artificial\nIntelligence. Edward Elgar Publishing.\n\n\nLindholm, Andreas, Dave Zachariah, Petre Stoica, and Thomas B. Schon.\n2019. “Data Consistency Approach to Model Validation.”\n#IEEE_O_ACC# 7: 59788–96. https://doi.org/10.1109/access.2019.2915109.\n\n\nLindholm, Erik, John Nickolls, Stuart Oberman, and John Montrym. 2008.\n“NVIDIA Tesla: A Unified Graphics and\nComputing Architecture.” IEEE Micro 28 (2): 39–55. https://doi.org/10.1109/mm.2008.31.\n\n\nLin, Tang Tang, Dang Yang, and Han Gan. 2023. “AWQ:\nActivation-aware Weight Quantization for\nLLM Compression and Acceleration.” ArXiv\nPreprint. https://arxiv.org/abs/2306.00978.\n\n\nLiu, Yanan, Xiaoxia Wei, Jinyu Xiao, Zhijie Liu, Yang Xu, and Yun Tian.\n2020. “Energy Consumption and Emission Mitigation Prediction Based\non Data Center Traffic and PUE for Global Data\nCenters.” Global Energy Interconnection 3 (3): 272–82.\nhttps://doi.org/10.1016/j.gloei.2020.07.008.\n\n\nLiu, Yingcheng, Guo Zhang, Christopher G. Tarolli, Rumen Hristov, Stella\nJensen-Roberts, Emma M. Waddell, Taylor L. Myers, et al. 2022.\n“Monitoring Gait at Home with Radio Waves in\nParkinson’s Disease: A Marker of Severity,\nProgression, and Medication Response.” Sci. Transl. Med.\n14 (663): eadc9669. https://doi.org/10.1126/scitranslmed.adc9669.\n\n\nLoh, Gabriel H. 2008. “3D-Stacked Memory\nArchitectures for Multi-Core Processors.” ACM SIGARCH\nComputer Architecture News 36 (3): 453–64. https://doi.org/10.1145/1394608.1382159.\n\n\nLopez-Paz, David, and Marc’Aurelio Ranzato. 2017. “Gradient\nEpisodic Memory for Continual Learning.” Adv Neural Inf\nProcess Syst 30.\n\n\nLou, Yin, Rich Caruana, Johannes Gehrke, and Giles Hooker. 2013.\n“Accurate Intelligible Models with Pairwise Interactions.”\nIn Proceedings of the 19th ACM SIGKDD International Conference on\nKnowledge Discovery and Data Mining, edited by Inderjit S. Dhillon,\nYehuda Koren, Rayid Ghani, Ted E. Senator, Paul Bradley, Rajesh Parekh,\nJingrui He, Robert L. Grossman, and Ramasamy Uthurusamy, 623–31. ACM. https://doi.org/10.1145/2487575.2487579.\n\n\nLowy, Andrew, Rakesh Pavan, Sina Baharlouei, Meisam Razaviyayn, and\nAhmad Beirami. 2021. “Fermi: Fair Empirical Risk\nMinimization via Exponential Rényi Mutual Information.”\n\n\nLubana, Ekdeep Singh, and Robert P Dick. 2020. “A Gradient Flow\nFramework for Analyzing Network Pruning.” arXiv Preprint\narXiv:2009.11839.\n\n\nLuebke, David. 2008. “CUDA: Scalable\nParallel Programming for High-Performance Scientific Computing.”\nIn 2008 5th IEEE International Symposium on Biomedical Imaging: From\nNano to Macro, 836–38. IEEE. https://doi.org/10.1109/isbi.2008.4541126.\n\n\nLundberg, Scott M., and Su-In Lee. 2017. “A Unified Approach to\nInterpreting Model Predictions.” In Advances in Neural\nInformation Processing Systems 30: Annual Conference on Neural\nInformation Processing Systems 2017, December 4-9, 2017, Long Beach, CA,\nUSA, edited by Isabelle Guyon, Ulrike von Luxburg, Samy Bengio,\nHanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett,\n4765–74. https://proceedings.neurips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html.\n\n\nMa, Dongning, Fred Lin, Alban Desmaison, Joel Coburn, Daniel Moore,\nSriram Sankar, and Xun Jiao. 2024. “Dr.\nDNA: Combating Silent Data Corruptions in Deep\nLearning Using Distribution of Neuron Activations.” In\nProceedings of the 29th ACM International Conference on\nArchitectural Support for Programming Languages and Operating Systems,\nVolume 3, 239–52. ACM. https://doi.org/10.1145/3620666.3651349.\n\n\nMaas, Martin, David G. Andersen, Michael Isard, Mohammad Mahdi\nJavanmard, Kathryn S. McKinley, and Colin Raffel. 2024. “Combining\nMachine Learning and Lifetime-Based Resource Management for Memory\nAllocation and Beyond.” Commun. ACM 67 (4): 87–96. https://doi.org/10.1145/3611018.\n\n\nMaass, Wolfgang. 1997. “Networks of Spiking Neurons:\nThe Third Generation of Neural Network Models.”\nNeural Networks 10 (9): 1659–71. https://doi.org/10.1016/s0893-6080(97)00011-7.\n\n\nMadry, Aleksander, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras,\nand Adrian Vladu. 2017. “Towards Deep Learning Models Resistant to\nAdversarial Attacks.” arXiv Preprint arXiv:1706.06083.\n\n\nMahmoud, Abdulrahman, Neeraj Aggarwal, Alex Nobbe, Jose Rodrigo Sanchez\nVicarte, Sarita V. Adve, Christopher W. Fletcher, Iuri Frosio, and Siva\nKumar Sastry Hari. 2020. “PyTorchFI: A\nRuntime Perturbation Tool for DNNs.” In 2020\n50th Annual IEEE/IFIP International Conference on Dependable Systems and\nNetworks Workshops (DSN-w), 25–31. IEEE; IEEE. https://doi.org/10.1109/dsn-w50199.2020.00014.\n\n\nMahmoud, Abdulrahman, Siva Kumar Sastry Hari, Christopher W. Fletcher,\nSarita V. Adve, Charbel Sakr, Naresh Shanbhag, Pavlo Molchanov, Michael\nB. Sullivan, Timothy Tsai, and Stephen W. Keckler. 2021.\n“Optimizing Selective Protection for CNN\nResilience.” In 2021 IEEE 32nd International Symposium on\nSoftware Reliability Engineering (ISSRE), 127–38. IEEE. https://doi.org/10.1109/issre52982.2021.00025.\n\n\nMahmoud, Abdulrahman, Thierry Tambe, Tarek Aloui, David Brooks, and\nGu-Yeon Wei. 2022. “GoldenEye: A\nPlatform for Evaluating Emerging Numerical Data Formats in\nDNN Accelerators.” In 2022 52nd Annual IEEE/IFIP\nInternational Conference on Dependable Systems and Networks (DSN),\n206–14. IEEE. https://doi.org/10.1109/dsn53405.2022.00031.\n\n\nMarković, Danijela, Alice Mizrahi, Damien Querlioz, and Julie Grollier.\n2020. “Physics for Neuromorphic Computing.” Nature\nReviews Physics 2 (9): 499–510. https://doi.org/10.1038/s42254-020-0208-2.\n\n\nMartin, C. Dianne. 1993. “The Myth of the Awesome Thinking\nMachine.” Commun. ACM 36 (4): 120–33. https://doi.org/10.1145/255950.153587.\n\n\nMarulli, Fiammetta, Stefano Marrone, and Laura Verde. 2022.\n“Sensitivity of Machine Learning Approaches to Fake and Untrusted\nData in Healthcare Domain.” Journal of Sensor and Actuator\nNetworks 11 (2): 21. https://doi.org/10.3390/jsan11020021.\n\n\nMaslej, Nestor, Loredana Fattorini, Erik Brynjolfsson, John Etchemendy,\nKatrina Ligett, Terah Lyons, James Manyika, et al. 2023.\n“Artificial Intelligence Index Report 2023.” ArXiv\nPreprint abs/2310.03715. https://arxiv.org/abs/2310.03715.\n\n\nMattson, Peter, Vijay Janapa Reddi, Christine Cheng, Cody Coleman, Greg\nDiamos, David Kanter, Paulius Micikevicius, et al. 2020a.\n“MLPerf: An Industry Standard Benchmark\nSuite for Machine Learning Performance.” IEEE Micro 40\n(2): 8–16. https://doi.org/10.1109/mm.2020.2974843.\n\n\n———, et al. 2020b. “MLPerf: An Industry\nStandard Benchmark Suite for Machine Learning Performance.”\nIEEE Micro 40 (2): 8–16. https://doi.org/10.1109/mm.2020.2974843.\n\n\nMazumder, Mark, Sharad Chitlangia, Colby Banbury, Yiping Kang, Juan\nManuel Ciro, Keith Achorn, Daniel Galvez, et al. 2021.\n“Multilingual Spoken Words Corpus.” In Thirty-Fifth\nConference on Neural Information Processing Systems Datasets and\nBenchmarks Track (Round 2).\n\n\nMcCarthy, John. 1981. “Epistemological Problems of Artificial\nIntelligence.” In Readings in Artificial Intelligence,\n459–65. Elsevier. https://doi.org/10.1016/b978-0-934613-03-3.50035-0.\n\n\nMcMahan, Brendan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise\nAgüera y Arcas. 2017. “Communication-Efficient Learning of Deep\nNetworks from Decentralized Data.” In Proceedings of the 20th\nInternational Conference on Artificial Intelligence and Statistics,\nAISTATS 2017, 20-22 April 2017, Fort Lauderdale, FL, USA, edited by\nAarti Singh and Xiaojin (Jerry) Zhu, 54:1273–82. Proceedings of Machine\nLearning Research. PMLR. http://proceedings.mlr.press/v54/mcmahan17a.html.\n\n\nMiller, Charlie. 2019. “Lessons Learned from Hacking a\nCar.” IEEE Design &Amp; Test 36 (6): 7–9. https://doi.org/10.1109/mdat.2018.2863106.\n\n\nMiller, Charlie, and Chris Valasek. 2015. “Remote Exploitation of\nan Unaltered Passenger Vehicle.” Black Hat USA 2015 (S\n91): 1–91.\n\n\nMiller, D. A. B. 2000. “Optical Interconnects to Silicon.”\n#IEEE_J_JSTQE# 6 (6): 1312–17. https://doi.org/10.1109/2944.902184.\n\n\nMills, Andrew, and Stephen Le Hunte. 1997. “An Overview of\nSemiconductor Photocatalysis.” J. Photochem. Photobiol.,\nA 108 (1): 1–35. https://doi.org/10.1016/s1010-6030(97)00118-4.\n\n\nMirhoseini, Azalia, Anna Goldie, Mustafa Yazgan, Joe Wenjie Jiang,\nEbrahim Songhori, Shen Wang, Young-Joon Lee, et al. 2021. “A Graph\nPlacement Methodology for Fast Chip Design.” Nature 594\n(7862): 207–12. https://doi.org/10.1038/s41586-021-03544-w.\n\n\nMishra, Asit K., Jorge Albericio Latorre, Jeff Pool, Darko Stosic, Dusan\nStosic, Ganesh Venkatesh, Chong Yu, and Paulius Micikevicius. 2021.\n“Accelerating Sparse Deep Neural Networks.” CoRR\nabs/2104.08378. https://arxiv.org/abs/2104.08378.\n\n\nMittal, Sparsh, Gaurav Verma, Brajesh Kaushik, and Farooq A. Khanday.\n2021. “A Survey of SRAM-Based in-Memory Computing\nTechniques and Applications.” J. Syst. Architect. 119\n(October): 102276. https://doi.org/10.1016/j.sysarc.2021.102276.\n\n\nModha, Dharmendra S., Filipp Akopyan, Alexander Andreopoulos,\nRathinakumar Appuswamy, John V. Arthur, Andrew S. Cassidy, Pallab Datta,\net al. 2023. “Neural Inference at the Frontier of Energy, Space,\nand Time.” Science 382 (6668): 329–35. https://doi.org/10.1126/science.adh1174.\n\n\nMohanram, K., and N. A. Touba. 2003. “Partial Error Masking to\nReduce Soft Error Failure Rate in Logic Circuits.” In\nProceedings. 16th IEEE Symposium on Computer Arithmetic,\n433–40. IEEE; IEEE Comput. Soc. https://doi.org/10.1109/dftvs.2003.1250141.\n\n\nMonyei, Chukwuka G., and Kirsten E. H. Jenkins. 2018. “Electrons\nHave No Identity: Setting Right Misrepresentations in\nGoogle and Apple’s Clean Energy Purchasing.”\nEnergy Research &Amp; Social Science 46 (December): 48–51.\nhttps://doi.org/10.1016/j.erss.2018.06.015.\n\n\nMoshawrab, Mohammad, Mehdi Adda, Abdenour Bouzouane, Hussein Ibrahim,\nand Ali Raad. 2023. “Reviewing Federated Learning Aggregation\nAlgorithms; Strategies, Contributions, Limitations and Future\nPerspectives.” Electronics 12 (10): 2287. https://doi.org/10.3390/electronics12102287.\n\n\nMukherjee, S. S., J. Emer, and S. K. Reinhardt. 2005. “The Soft\nError Problem: An Architectural Perspective.” In\n11th International Symposium on High-Performance Computer\nArchitecture, 243–47. IEEE; IEEE. https://doi.org/10.1109/hpca.2005.37.\n\n\nMunshi, Aaftab. 2009. “The OpenCL\nSpecification.” In 2009 IEEE Hot Chips 21 Symposium\n(HCS), 1–314. IEEE. https://doi.org/10.1109/hotchips.2009.7478342.\n\n\nMusk, Elon et al. 2019. “An Integrated Brain-Machine Interface\nPlatform with Thousands of Channels.” J. Med. Internet\nRes. 21 (10): e16194. https://doi.org/10.2196/16194.\n\n\nMyllyaho, Lalli, Mikko Raatikainen, Tomi Männistö, Jukka K. Nurminen,\nand Tommi Mikkonen. 2022. “On Misbehaviour and Fault Tolerance in\nMachine Learning Systems.” J. Syst. Software 183\n(January): 111096. https://doi.org/10.1016/j.jss.2021.111096.\n\n\nNakano, Jane. 2021. The Geopolitics of Critical Minerals Supply\nChains. JSTOR.\n\n\nNarayanan, Arvind, and Vitaly Shmatikov. 2006. “How to Break\nAnonymity of the Netflix Prize Dataset.” arXiv Preprint\nCs/0610105.\n\n\nNg, Davy Tsz Kit, Jac Ka Lok Leung, Kai Wah Samuel Chu, and Maggie Shen\nQiao. 2021. “AI Literacy: Definition,\nTeaching, Evaluation and Ethical Issues.” Proceedings of the\nAssociation for Information Science and Technology 58 (1): 504–9.\n\n\nNgo, Richard, Lawrence Chan, and Sören Mindermann. 2022. “The\nAlignment Problem from a Deep Learning Perspective.” ArXiv\nPreprint abs/2209.00626. https://arxiv.org/abs/2209.00626.\n\n\nNguyen, Ngoc-Bao, Keshigeyan Chandrasegaran, Milad Abdollahzadeh, and\nNgai-Man Cheung. 2023. “Re-Thinking Model Inversion Attacks\nAgainst Deep Neural Networks.” In 2023 IEEE/CVF Conference on\nComputer Vision and Pattern Recognition (CVPR), 16384–93. IEEE. https://doi.org/10.1109/cvpr52729.2023.01572.\n\n\nNorrie, Thomas, Nishant Patil, Doe Hyun Yoon, George Kurian, Sheng Li,\nJames Laudon, Cliff Young, Norman Jouppi, and David Patterson. 2021.\n“The Design Process for Google’s Training Chips:\nTpuv2 and TPUv3.” IEEE Micro\n41 (2): 56–63. https://doi.org/10.1109/mm.2021.3058217.\n\n\nNorthcutt, Curtis G, Anish Athalye, and Jonas Mueller. 2021.\n“Pervasive Label Errors in Test Sets Destabilize Machine Learning\nBenchmarks.” arXiv. https://doi.org/https://doi.org/10.48550/arXiv.2103.14749\narXiv-issued DOI via DataCite.\n\n\nObermeyer, Ziad, Brian Powers, Christine Vogeli, and Sendhil\nMullainathan. 2019. “Dissecting Racial Bias in an Algorithm Used\nto Manage the Health of Populations.” Science 366\n(6464): 447–53. https://doi.org/10.1126/science.aax2342.\n\n\nOecd. 2023. “A Blueprint for Building National Compute Capacity\nfor Artificial Intelligence.” 350. Organisation for Economic\nCo-Operation; Development (OECD). https://doi.org/10.1787/876367e3-en.\n\n\nOlah, Chris, Nick Cammarata, Ludwig Schubert, Gabriel Goh, Michael\nPetrov, and Shan Carter. 2020. “Zoom in: An\nIntroduction to Circuits.” Distill 5 (3): e00024–001. https://doi.org/10.23915/distill.00024.001.\n\n\nOliynyk, Daryna, Rudolf Mayer, and Andreas Rauber. 2023. “I Know\nWhat You Trained Last Summer: A Survey on Stealing Machine\nLearning Models and Defences.” ACM Comput. Surv. 55\n(14s): 1–41. https://doi.org/10.1145/3595292.\n\n\nOoko, Samson Otieno, Marvin Muyonga Ogore, Jimmy Nsenga, and Marco\nZennaro. 2021. “TinyML in Africa:\nOpportunities and Challenges.” In 2021 IEEE\nGlobecom Workshops (GC Wkshps), 1–6. IEEE; IEEE. https://doi.org/10.1109/gcwkshps52748.2021.9682107.\n\n\nOprea, Alina, Anoop Singhal, and Apostol Vassilev. 2022.\n“Poisoning Attacks Against Machine Learning: Can\nMachine Learning Be Trustworthy?” Computer 55 (11):\n94–99. https://doi.org/10.1109/mc.2022.3190787.\n\n\nPan, Sinno Jialin, and Qiang Yang. 2010. “A Survey on Transfer\nLearning.” IEEE Trans. Knowl. Data Eng. 22 (10):\n1345–59. https://doi.org/10.1109/tkde.2009.191.\n\n\nPanda, Priyadarshini, Indranil Chakraborty, and Kaushik Roy. 2019.\n“Discretization Based Solutions for Secure Machine Learning\nAgainst Adversarial Attacks.” #IEEE_O_ACC# 7: 70157–68.\nhttps://doi.org/10.1109/access.2019.2919463.\n\n\nPapadimitriou, George, and Dimitris Gizopoulos. 2021.\n“Demystifying the System Vulnerability Stack:\nTransient Fault Effects Across the Layers.” In\n2021 ACM/IEEE 48th Annual International Symposium on Computer\nArchitecture (ISCA), 902–15. IEEE; IEEE. https://doi.org/10.1109/isca52012.2021.00075.\n\n\nPapernot, Nicolas, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram\nSwami. 2016. “Distillation as a Defense to Adversarial\nPerturbations Against Deep Neural Networks.” In 2016 IEEE\nSymposium on Security and Privacy (SP), 582–97. IEEE; IEEE. https://doi.org/10.1109/sp.2016.41.\n\n\nParrish, Alicia, Hannah Rose Kirk, Jessica Quaye, Charvi Rastogi, Max\nBartolo, Oana Inel, Juan Ciro, et al. 2023. “Adversarial Nibbler:\nA Data-Centric Challenge for Improving the Safety of\nText-to-Image Models.” ArXiv Preprint abs/2305.14384. https://arxiv.org/abs/2305.14384.\n\n\nPatterson, David A, and John L Hennessy. 2016. Computer Organization\nand Design ARM Edition: The Hardware Software\nInterface. Morgan kaufmann.\n\n\nPatterson, David, Joseph Gonzalez, Urs Holzle, Quoc Le, Chen Liang,\nLluis-Miquel Munguia, Daniel Rothchild, David R. So, Maud Texier, and\nJeff Dean. 2022. “The Carbon Footprint of Machine Learning\nTraining Will Plateau, Then Shrink.” Computer 55 (7):\n18–28. https://doi.org/10.1109/mc.2022.3148714.\n\n\nPeters, Dorian, Rafael A. Calvo, and Richard M. Ryan. 2018.\n“Designing for Motivation, Engagement and Wellbeing in Digital\nExperience.” Front. Psychol. 9 (May): 797. https://doi.org/10.3389/fpsyg.2018.00797.\n\n\nPhillips, P Jonathon, Carina A Hahn, Peter C Fontana, David A\nBroniatowski, and Mark A Przybocki. 2020. “Four Principles of\nExplainable Artificial Intelligence.” Gaithersburg,\nMaryland 18.\n\n\nPlank, James S. 1997. “A Tutorial on\nReedSolomon Coding for Fault-Tolerance in\nRAID-Like Systems.” Software: Practice and\nExperience 27 (9): 995–1012.\n\n\nPont, Michael J, and Royan HL Ong. 2002. “Using Watchdog Timers to\nImprove the Reliability of Single-Processor Embedded Systems:\nSeven New Patterns and a Case Study.” In\nProceedings of the First Nordic Conference on Pattern Languages of\nPrograms, 159–200. Citeseer.\n\n\nPrakash, Shvetank, Tim Callahan, Joseph Bushagour, Colby Banbury, Alan\nV. Green, Pete Warden, Tim Ansell, and Vijay Janapa Reddi. 2023.\n“CFU Playground: Full-stack Open-Source Framework for Tiny Machine\nLearning (TinyML) Acceleration on\nFPGAs.” In 2023 IEEE International Symposium on\nPerformance Analysis of Systems and Software (ISPASS). Vol.\nabs/2201.01863. IEEE. https://doi.org/10.1109/ispass57527.2023.00024.\n\n\nPrakash, Shvetank, Matthew Stewart, Colby Banbury, Mark Mazumder, Pete\nWarden, Brian Plancher, and Vijay Janapa Reddi. 2023. “Is\nTinyML Sustainable? Assessing the Environmental Impacts of\nMachine Learning on Microcontrollers.” ArXiv Preprint.\nhttps://arxiv.org/abs/2301.11899.\n\n\nPsoma, Sotiria D., and Chryso Kanthou. 2023. “Wearable Insulin\nBiosensors for Diabetes Management: Advances and\nChallenges.” Biosensors 13 (7): 719. https://doi.org/10.3390/bios13070719.\n\n\nPushkarna, Mahima, Andrew Zaldivar, and Oddur Kjartansson. 2022.\n“Data Cards: Purposeful and Transparent Dataset\nDocumentation for Responsible AI.” In 2022 ACM\nConference on Fairness, Accountability, and Transparency. ACM. https://doi.org/10.1145/3531146.3533231.\n\n\nPutnam, Andrew, Adrian M. Caulfield, Eric S. Chung, Derek Chiou, Kypros\nConstantinides, John Demme, Hadi Esmaeilzadeh, et al. 2014. “A\nReconfigurable Fabric for Accelerating Large-Scale Datacenter\nServices.” ACM SIGARCH Computer Architecture News 42\n(3): 13–24. https://doi.org/10.1145/2678373.2665678.\n\n\nQi, Chen, Shibo Shen, Rongpeng Li, Zhifeng Zhao, Qing Liu, Jing Liang,\nand Honggang Zhang. 2021. “An Efficient Pruning Scheme of Deep\nNeural Networks for Internet of Things Applications.” EURASIP\nJournal on Advances in Signal Processing 2021 (1): 31. https://doi.org/10.1186/s13634-021-00744-4.\n\n\nQian, Yu, Xuegong Zhou, Hao Zhou, and Lingli Wang. 2024. “An\nEfficient Reinforcement Learning Based Framework for Exploring Logic\nSynthesis.” ACM Trans. Des. Autom. Electron. Syst. 29\n(2): 1–33. https://doi.org/10.1145/3632174.\n\n\nR. V., Rashmi, and Karthikeyan A. 2018. “Secure Boot of Embedded\nApplications - a Review.” In 2018 Second International\nConference on Electronics, Communication and Aerospace Technology\n(ICECA), 291–98. IEEE. https://doi.org/10.1109/iceca.2018.8474730.\n\n\nRachwan, John, Daniel Zügner, Bertrand Charpentier, Simon Geisler,\nMorgane Ayle, and Stephan Günnemann. 2022. “Winning the Lottery\nAhead of Time: Efficient Early Network Pruning.” In\nInternational Conference on Machine Learning, 18293–309. PMLR.\n\n\nRaina, Rajat, Anand Madhavan, and Andrew Y. Ng. 2009. “Large-Scale\nDeep Unsupervised Learning Using Graphics Processors.” In\nProceedings of the 26th Annual International Conference on Machine\nLearning, edited by Andrea Pohoreckyj Danyluk, Léon Bottou, and\nMichael L. Littman, 382:873–80. ACM International Conference Proceeding\nSeries. ACM. https://doi.org/10.1145/1553374.1553486.\n\n\nRamaswamy, Vikram V., Sunnie S. Y. Kim, Ruth Fong, and Olga Russakovsky.\n2023a. “Overlooked Factors in Concept-Based Explanations:\nDataset Choice, Concept Learnability, and Human\nCapability.” In 2023 IEEE/CVF Conference on Computer Vision\nand Pattern Recognition (CVPR), 10932–41. IEEE. https://doi.org/10.1109/cvpr52729.2023.01052.\n\n\nRamaswamy, Vikram V, Sunnie SY Kim, Ruth Fong, and Olga Russakovsky.\n2023b. “UFO: A Unified Method for\nControlling Understandability and Faithfulness Objectives in\nConcept-Based Explanations for CNNs.” ArXiv\nPreprint abs/2303.15632. https://arxiv.org/abs/2303.15632.\n\n\nRamcharan, Amanda, Kelsee Baranowski, Peter McCloskey, Babuali Ahmed,\nJames Legg, and David P. Hughes. 2017. “Deep Learning for\nImage-Based Cassava Disease Detection.” Front. Plant\nSci. 8 (October): 1852. https://doi.org/10.3389/fpls.2017.01852.\n\n\nRamesh, Aditya, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss,\nAlec Radford, Mark Chen, and Ilya Sutskever. 2021. “Zero-Shot\nText-to-Image Generation.” In Proceedings of the 38th\nInternational Conference on Machine Learning, ICML 2021, 18-24 July\n2021, Virtual Event, edited by Marina Meila and Tong Zhang,\n139:8821–31. Proceedings of Machine Learning Research. PMLR. http://proceedings.mlr.press/v139/ramesh21a.html.\n\n\nRanganathan, Parthasarathy. 2011. “From Microprocessors to\nNanostores: Rethinking Data-Centric Systems.”\nComputer 44 (1): 39–48. https://doi.org/10.1109/mc.2011.18.\n\n\nRao, Ravi. 2021. “TinyML Unlocks New Possibilities\nfor Sustainable Development Technologies.”\nWww.wevolver.com. https://www.wevolver.com/article/tinyml-unlocks-new-possibilities-for-sustainable-development-technologies.\n\n\nRashid, Layali, Karthik Pattabiraman, and Sathish Gopalakrishnan. 2012.\n“Intermittent Hardware Errors Recovery: Modeling and\nEvaluation.” In 2012 Ninth International Conference on\nQuantitative Evaluation of Systems, 220–29. IEEE; IEEE. https://doi.org/10.1109/qest.2012.37.\n\n\n———. 2015. “Characterizing the Impact of Intermittent Hardware\nFaults on Programs.” IEEE Trans. Reliab. 64 (1):\n297–310. https://doi.org/10.1109/tr.2014.2363152.\n\n\nRatner, Alex, Braden Hancock, Jared Dunnmon, Roger Goldman, and\nChristopher Ré. 2018. “Snorkel MeTaL: Weak\nSupervision for Multi-Task Learning.” In Proceedings of the\nSecond Workshop on Data Management for End-to-End Machine Learning.\nACM. https://doi.org/10.1145/3209889.3209898.\n\n\nReagen, Brandon, Udit Gupta, Lillian Pentecost, Paul Whatmough, Sae Kyu\nLee, Niamh Mulholland, David Brooks, and Gu-Yeon Wei. 2018. “Ares:\nA Framework for Quantifying the Resilience of Deep Neural\nNetworks.” In 2018 55th ACM/ESDA/IEEE Design Automation\nConference (DAC), 1–6. IEEE. https://doi.org/10.1109/dac.2018.8465834.\n\n\nReagen, Brandon, Jose Miguel Hernandez-Lobato, Robert Adolf, Michael\nGelbart, Paul Whatmough, Gu-Yeon Wei, and David Brooks. 2017. “A\nCase for Efficient Accelerator Design Space Exploration via\nBayesian Optimization.” In 2017 IEEE/ACM\nInternational Symposium on Low Power Electronics and Design\n(ISLPED), 1–6. IEEE; IEEE. https://doi.org/10.1109/islped.2017.8009208.\n\n\nReddi, Sashank J., Satyen Kale, and Sanjiv Kumar. 2019. “On the\nConvergence of Adam and Beyond.” arXiv Preprint\narXiv:1904.09237, April. http://arxiv.org/abs/1904.09237v1.\n\n\nReddi, Vijay Janapa, Christine Cheng, David Kanter, Peter Mattson,\nGuenther Schmuelling, Carole-Jean Wu, Brian Anderson, et al. 2020.\n“MLPerf Inference Benchmark.” In 2020\nACM/IEEE 47th Annual International Symposium on Computer Architecture\n(ISCA), 446–59. IEEE; IEEE. https://doi.org/10.1109/isca45697.2020.00045.\n\n\nReddi, Vijay Janapa, and Meeta Sharma Gupta. 2013. Resilient\nArchitecture Design for Voltage Variation. Springer International\nPublishing. https://doi.org/10.1007/978-3-031-01739-1.\n\n\nReis, G. A., J. Chang, N. Vachharajani, R. Rangan, and D. I. August.\n2005. “SWIFT: Software Implemented Fault\nTolerance.” In International Symposium on Code Generation and\nOptimization, 243–54. IEEE; IEEE. https://doi.org/10.1109/cgo.2005.34.\n\n\nRibeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. 2016.\n“” Why Should i Trust You?” Explaining\nthe Predictions of Any Classifier.” In Proceedings of the\n22nd ACM SIGKDD International Conference on Knowledge Discovery and Data\nMining, 1135–44.\n\n\nRobbins, Herbert, and Sutton Monro. 1951. “A Stochastic\nApproximation Method.” The Annals of Mathematical\nStatistics 22 (3): 400–407. https://doi.org/10.1214/aoms/1177729586.\n\n\nRombach, Robin, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and\nBjorn Ommer. 2022. “High-Resolution Image Synthesis with Latent\nDiffusion Models.” In 2022 IEEE/CVF Conference on Computer\nVision and Pattern Recognition (CVPR). IEEE. https://doi.org/10.1109/cvpr52688.2022.01042.\n\n\nRosa, Gustavo H. de, and João P. Papa. 2021. “A Survey on Text\nGeneration Using Generative Adversarial Networks.” Pattern\nRecogn. 119 (November): 108098. https://doi.org/10.1016/j.patcog.2021.108098.\n\n\nRosenblatt, Frank. 1957. The Perceptron, a Perceiving and\nRecognizing Automaton Project Para. Cornell Aeronautical\nLaboratory.\n\n\nRoskies, Adina. 2002. “Neuroethics for the New Millenium.”\nNeuron 35 (1): 21–23. https://doi.org/10.1016/s0896-6273(02)00763-8.\n\n\nRuder, Sebastian. 2016. “An Overview of Gradient Descent\nOptimization Algorithms.” ArXiv Preprint abs/1609.04747\n(September). http://arxiv.org/abs/1609.04747v2.\n\n\nRudin, Cynthia. 2019. “Stop Explaining Black Box Machine Learning\nModels for High Stakes Decisions and Use Interpretable Models\nInstead.” Nature Machine Intelligence 1 (5): 206–15. https://doi.org/10.1038/s42256-019-0048-x.\n\n\nRumelhart, David E., Geoffrey E. Hinton, and Ronald J. Williams. 1986.\n“Learning Representations by Back-Propagating Errors.”\nNature 323 (6088): 533–36. https://doi.org/10.1038/323533a0.\n\n\nRussakovsky, Olga, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh,\nSean Ma, Zhiheng Huang, et al. 2015. “ImageNet Large\nScale Visual Recognition Challenge.” Int. J. Comput.\nVision 115 (3): 211–52. https://doi.org/10.1007/s11263-015-0816-y.\n\n\nRussell, Stuart. 2021. “Human-Compatible Artificial\nIntelligence.” Human-Like Machine Intelligence, 3–23.\n\n\nRyan, Richard M., and Edward L. Deci. 2000. “Self-Determination\nTheory and the Facilitation of Intrinsic Motivation, Social Development,\nand Well-Being.” Am. Psychol. 55 (1): 68–78. https://doi.org/10.1037/0003-066x.55.1.68.\n\n\nSamajdar, Ananda, Yuhao Zhu, Paul Whatmough, Matthew Mattina, and Tushar\nKrishna. 2018. “Scale-Sim: Systolic Cnn Accelerator\nSimulator.” ArXiv Preprint abs/1811.02883. https://arxiv.org/abs/1811.02883.\n\n\nSambasivan, Nithya, Shivani Kapania, Hannah Highfill, Diana Akrong,\nPraveen Paritosh, and Lora M Aroyo. 2021a.\n““Everyone Wants to Do the Model Work,\nNot the Data Work”: Data Cascades in\nHigh-Stakes AI.” In Proceedings of the 2021 CHI\nConference on Human Factors in Computing Systems, 1–15.\n\n\n———. 2021b. ““Everyone Wants to Do the\nModel Work, Not the Data Work”: Data Cascades\nin High-Stakes AI.” In Proceedings of the 2021\nCHI Conference on Human Factors in Computing Systems. CHI ’21. New\nYork, NY, USA: ACM. https://doi.org/10.1145/3411764.3445518.\n\n\n———. 2021c. ““Everyone Wants to Do the\nModel Work, Not the Data Work”: Data Cascades\nin High-Stakes AI.” In Proceedings of the 2021\nCHI Conference on Human Factors in Computing Systems. ACM. https://doi.org/10.1145/3411764.3445518.\n\n\nSangchoolie, Behrooz, Karthik Pattabiraman, and Johan Karlsson. 2017.\n“One Bit Is (Not) Enough: An Empirical\nStudy of the Impact of Single and Multiple Bit-Flip Errors.” In\n2017 47th Annual IEEE/IFIP International Conference on Dependable\nSystems and Networks (DSN), 97–108. IEEE; IEEE. https://doi.org/10.1109/dsn.2017.30.\n\n\nSchäfer, Mike S. 2023. “The Notorious GPT:\nScience Communication in the Age of Artificial\nIntelligence.” Journal of Science Communication 22 (02):\nY02. https://doi.org/10.22323/2.22020402.\n\n\nSchizas, Nikolaos, Aristeidis Karras, Christos Karras, and Spyros\nSioutas. 2022. “TinyML for Ultra-Low Power\nAI and Large Scale IoT Deployments:\nA Systematic Review.” Future Internet 14\n(12): 363. https://doi.org/10.3390/fi14120363.\n\n\nSchuman, Catherine D., Shruti R. Kulkarni, Maryam Parsa, J. Parker\nMitchell, Prasanna Date, and Bill Kay. 2022. “Opportunities for\nNeuromorphic Computing Algorithms and Applications.” Nature\nComputational Science 2 (1): 10–19. https://doi.org/10.1038/s43588-021-00184-y.\n\n\nSchwartz, Daniel, Jonathan Michael Gomes Selman, Peter Wrege, and\nAndreas Paepcke. 2021. “Deployment of Embedded\nEdge-AI for Wildlife Monitoring in Remote Regions.”\nIn 2021 20th IEEE International Conference on Machine Learning and\nApplications (ICMLA), 1035–42. IEEE; IEEE. https://doi.org/10.1109/icmla52953.2021.00170.\n\n\nSchwartz, Roy, Jesse Dodge, Noah A. Smith, and Oren Etzioni. 2020.\n“Green AI.” Commun. ACM 63 (12):\n54–63. https://doi.org/10.1145/3381831.\n\n\nSegal, Mark, and Kurt Akeley. 1999. “The OpenGL\nGraphics System: A Specification (Version 1.1).”\n\n\nSegura Anaya, L. H., Abeer Alsadoon, N. Costadopoulos, and P. W. C.\nPrasad. 2017. “Ethical Implications of User Perceptions of\nWearable Devices.” Sci. Eng. Ethics 24 (1): 1–28. https://doi.org/10.1007/s11948-017-9872-8.\n\n\nSeide, Frank, and Amit Agarwal. 2016. “Cntk: Microsoft’s\nOpen-Source Deep-Learning Toolkit.” In Proceedings of the\n22nd ACM SIGKDD International Conference on Knowledge Discovery and Data\nMining, 2135–35. ACM. https://doi.org/10.1145/2939672.2945397.\n\n\nSelvaraju, Ramprasaath R., Michael Cogswell, Abhishek Das, Ramakrishna\nVedantam, Devi Parikh, and Dhruv Batra. 2017.\n“Grad-CAM: Visual Explanations from Deep\nNetworks via Gradient-Based Localization.” In 2017 IEEE\nInternational Conference on Computer Vision (ICCV), 618–26. IEEE.\nhttps://doi.org/10.1109/iccv.2017.74.\n\n\nSeong, Nak Hee, Dong Hyuk Woo, Vijayalakshmi Srinivasan, Jude A. Rivers,\nand Hsien-Hsin S. Lee. 2010. “SAFER: Stuck-at-fault Error Recovery for\nMemories.” In 2010 43rd Annual IEEE/ACM International\nSymposium on Microarchitecture, 115–24. IEEE; IEEE. https://doi.org/10.1109/micro.2010.46.\n\n\nSeyedzadeh, Saleh, Farzad Pour Rahimian, Ivan Glesk, and Marc Roper.\n2018. “Machine Learning for Estimation of Building Energy\nConsumption and Performance: A Review.”\nVisualization in Engineering 6 (1): 1–20. https://doi.org/10.1186/s40327-018-0064-7.\n\n\nShalev-Shwartz, Shai, Shaked Shammah, and Amnon Shashua. 2017. “On\na Formal Model of Safe and Scalable Self-Driving Cars.” ArXiv\nPreprint abs/1708.06374. https://arxiv.org/abs/1708.06374.\n\n\nShan, Shawn, Wenxin Ding, Josephine Passananti, Haitao Zheng, and Ben Y\nZhao. 2023. “Prompt-Specific Poisoning Attacks on Text-to-Image\nGenerative Models.” ArXiv Preprint abs/2310.13828. https://arxiv.org/abs/2310.13828.\n\n\nShastri, Bhavin J., Alexander N. Tait, T. Ferreira de Lima, Wolfram H.\nP. Pernice, Harish Bhaskaran, C. D. Wright, and Paul R. Prucnal. 2021.\n“Photonics for Artificial Intelligence and Neuromorphic\nComputing.” Nat. Photonics 15 (2): 102–14. https://doi.org/10.1038/s41566-020-00754-y.\n\n\nSheaffer, Jeremy W, David P Luebke, and Kevin Skadron. 2007. “A\nHardware Redundancy and Recovery Mechanism for Reliable Scientific\nComputation on Graphics Processors.” In Graphics\nHardware, 2007:55–64. Citeseer.\n\n\nShehabi, Arman, Sarah Smith, Dale Sartor, Richard Brown, Magnus Herrlin,\nJonathan Koomey, Eric Masanet, Nathaniel Horner, Inês Azevedo, and\nWilliam Lintner. 2016. “United States Data Center Energy Usage\nReport.”\n\n\nShen, Sheng, Zhen Dong, Jiayu Ye, Linjian Ma, Zhewei Yao, Amir Gholami,\nMichael W. Mahoney, and Kurt Keutzer. 2020. “Q-BERT:\nHessian Based Ultra Low Precision Quantization of\nBERT.” Proceedings of the AAAI Conference on\nArtificial Intelligence 34 (05): 8815–21. https://doi.org/10.1609/aaai.v34i05.6409.\n\n\nSheng, Victor S., and Jing Zhang. 2019. “Machine Learning with\nCrowdsourcing: A Brief Summary of the Past Research and\nFuture Directions.” Proceedings of the AAAI Conference on\nArtificial Intelligence 33 (01): 9837–43. https://doi.org/10.1609/aaai.v33i01.33019837.\n\n\nShi, Hongrui, and Valentin Radu. 2022. “Data Selection for\nEfficient Model Update in Federated Learning.” In Proceedings\nof the 2nd European Workshop on Machine Learning and Systems,\n72–78. ACM. https://doi.org/10.1145/3517207.3526980.\n\n\nShneiderman, Ben. 2020. “Bridging the Gap Between Ethics and\nPractice: Guidelines for Reliable, Safe, and Trustworthy Human-Centered\nAI Systems.” ACM Trans. Interact. Intell. Syst. 10 (4):\n1–31. https://doi.org/10.1145/3419764.\n\n\n———. 2022. Human-Centered AI. Oxford University\nPress.\n\n\nShokri, Reza, Marco Stronati, Congzheng Song, and Vitaly Shmatikov.\n2017. “Membership Inference Attacks Against Machine Learning\nModels.” In 2017 IEEE Symposium on Security and Privacy\n(SP), 3–18. IEEE; IEEE. https://doi.org/10.1109/sp.2017.41.\n\n\nSiddik, Md Abu Bakar, Arman Shehabi, and Landon Marston. 2021.\n“The Environmental Footprint of Data Centers in the United\nStates.” Environ. Res. Lett. 16 (6): 064017. https://doi.org/10.1088/1748-9326/abfba1.\n\n\nSilvestro, Daniele, Stefano Goria, Thomas Sterner, and Alexandre\nAntonelli. 2022. “Improving Biodiversity Protection Through\nArtificial Intelligence.” Nature Sustainability 5 (5):\n415–24. https://doi.org/10.1038/s41893-022-00851-6.\n\n\nSingh, Narendra, and Oladele A. Ogunseitan. 2022. “Disentangling\nthe Worldwide Web of e-Waste and Climate Change Co-Benefits.”\nCircular Economy 1 (2): 100011. https://doi.org/10.1016/j.cec.2022.100011.\n\n\nSkorobogatov, Sergei. 2009. “Local Heating Attacks on Flash Memory\nDevices.” In 2009 IEEE International Workshop on\nHardware-Oriented Security and Trust, 1–6. IEEE; IEEE. https://doi.org/10.1109/hst.2009.5225028.\n\n\nSkorobogatov, Sergei P, and Ross J Anderson. 2003. “Optical Fault\nInduction Attacks.” In Cryptographic Hardware and Embedded\nSystems-CHES 2002: 4th International Workshop Redwood Shores, CA, USA,\nAugust 1315, 2002 Revised Papers 4, 2–12. Springer.\n\n\nSmilkov, Daniel, Nikhil Thorat, Been Kim, Fernanda Viégas, and Martin\nWattenberg. 2017. “Smoothgrad: Removing Noise by\nAdding Noise.” ArXiv Preprint abs/1706.03825. https://arxiv.org/abs/1706.03825.\n\n\nSnoek, Jasper, Hugo Larochelle, and Ryan P. Adams. 2012.\n“Practical Bayesian Optimization of Machine Learning\nAlgorithms.” In Advances in Neural Information Processing\nSystems 25: 26th Annual Conference on Neural Information Processing\nSystems 2012. Proceedings of a Meeting Held December 3-6, 2012, Lake\nTahoe, Nevada, United States, edited by Peter L. Bartlett, Fernando\nC. N. Pereira, Christopher J. C. Burges, Léon Bottou, and Kilian Q.\nWeinberger, 2960–68. https://proceedings.neurips.cc/paper/2012/hash/05311655a15b75fab86956663e1819cd-Abstract.html.\n\n\nSrivastava, Nitish, Geoffrey E. Hinton, Alex Krizhevsky, Ilya Sutskever,\nand Ruslan Salakhutdinov. 2014. “Dropout: A Simple Way to Prevent\nNeural Networks from Overfitting.” J. Mach. Learn. Res.\n15 (1): 1929–58. https://doi.org/10.5555/2627435.2670313.\n\n\nStm32L4Q5Ag. 2021. STMicroelectronics.\n\n\nStrubell, Emma, Ananya Ganesh, and Andrew McCallum. 2019. “Energy\nand Policy Considerations for Deep Learning in NLP.”\nIn Proceedings of the 57th Annual Meeting of the Association for\nComputational Linguistics, 3645–50. Florence, Italy: Association\nfor Computational Linguistics. https://doi.org/10.18653/v1/p19-1355.\n\n\nSuda, Naveen, Vikas Chandra, Ganesh Dasika, Abinash Mohanty, Yufei Ma,\nSarma Vrudhula, Jae-sun Seo, and Yu Cao. 2016.\n“Throughput-Optimized OpenCL-Based FPGA\nAccelerator for Large-Scale Convolutional Neural Networks.” In\nProceedings of the 2016 ACM/SIGDA International Symposium on\nField-Programmable Gate Arrays, 16–25. ACM. https://doi.org/10.1145/2847263.2847276.\n\n\nSudhakar, Soumya, Vivienne Sze, and Sertac Karaman. 2023. “Data\nCenters on Wheels: Emissions from Computing Onboard\nAutonomous Vehicles.” IEEE Micro 43 (1): 29–39. https://doi.org/10.1109/mm.2022.3219803.\n\n\nSze, Vivienne, Yu-Hsin Chen, Tien-Ju Yang, and Joel S. Emer. 2017.\n“Efficient Processing of Deep Neural Networks: A\nTutorial and Survey.” Proc. IEEE 105 (12): 2295–2329. https://doi.org/10.1109/jproc.2017.2761740.\n\n\nSzegedy, Christian, Wojciech Zaremba, Ilya Sutskever, Joan Bruna,\nDumitru Erhan, Ian J. Goodfellow, and Rob Fergus. 2014.\n“Intriguing Properties of Neural Networks.” In 2nd\nInternational Conference on Learning Representations, ICLR 2014, Banff,\nAB, Canada, April 14-16, 2014, Conference Track Proceedings, edited\nby Yoshua Bengio and Yann LeCun. http://arxiv.org/abs/1312.6199.\n\n\nTambe, Thierry, En-Yu Yang, Zishen Wan, Yuntian Deng, Vijay Janapa\nReddi, Alexander Rush, David Brooks, and Gu-Yeon Wei. 2020.\n“Algorithm-Hardware Co-Design of Adaptive Floating-Point Encodings\nfor Resilient Deep Learning Inference.” In 2020 57th ACM/IEEE\nDesign Automation Conference (DAC), 1–6. IEEE; IEEE. https://doi.org/10.1109/dac18072.2020.9218516.\n\n\nTan, Mingxing, Bo Chen, Ruoming Pang, Vijay Vasudevan, Mark Sandler,\nAndrew Howard, and Quoc V. Le. 2019. “MnasNet: Platform-aware Neural Architecture Search for\nMobile.” In 2019 IEEE/CVF Conference on Computer Vision and\nPattern Recognition (CVPR), 2820–28. IEEE. https://doi.org/10.1109/cvpr.2019.00293.\n\n\nTan, Mingxing, and Quoc V. Le. 2023. “Demystifying Deep\nLearning.” Wiley. https://doi.org/10.1002/9781394205639.ch6.\n\n\nTang, Xin, Yichun He, and Jia Liu. 2022. “Soft Bioelectronics for\nCardiac Interfaces.” Biophysics Reviews 3 (1). https://doi.org/10.1063/5.0069516.\n\n\nTang, Xin, Hao Shen, Siyuan Zhao, Na Li, and Jia Liu. 2023.\n“Flexible Braincomputer Interfaces.”\nNature Electronics 6 (2): 109–18. https://doi.org/10.1038/s41928-022-00913-9.\n\n\nTarun, Ayush K, Vikram S Chundawat, Murari Mandal, and Mohan\nKankanhalli. 2022. “Deep Regression Unlearning.” ArXiv\nPreprint abs/2210.08196. https://arxiv.org/abs/2210.08196.\n\n\nTeam, The Theano Development, Rami Al-Rfou, Guillaume Alain, Amjad\nAlmahairi, Christof Angermueller, Dzmitry Bahdanau, Nicolas Ballas, et\nal. 2016. “Theano: A Python Framework for Fast\nComputation of Mathematical Expressions.” https://arxiv.org/abs/1605.02688.\n\n\n“The Ultimate Guide to Deep Learning Model Quantization and\nQuantization-Aware Training.” n.d. https://deci.ai/quantization-and-quantization-aware-training/.\n\n\nThompson, Neil C., Kristjan Greenewald, Keeheon Lee, and Gabriel F.\nManso. 2021. “Deep Learning’s Diminishing Returns:\nThe Cost of Improvement Is Becoming Unsustainable.”\nIEEE Spectr. 58 (10): 50–55. https://doi.org/10.1109/mspec.2021.9563954.\n\n\nTill, Aaron, Andrew L. Rypel, Andrew Bray, and Samuel B. Fey. 2019.\n“Fish Die-Offs Are Concurrent with Thermal Extremes in North\nTemperate Lakes.” Nat. Clim. Change 9 (8): 637–41. https://doi.org/10.1038/s41558-019-0520-y.\n\n\nTirtalistyani, Rose, Murtiningrum Murtiningrum, and Rameshwar S. Kanwar.\n2022. “Indonesia Rice Irrigation System:\nTime for Innovation.” Sustainability 14\n(19): 12477. https://doi.org/10.3390/su141912477.\n\n\nTokui, Seiya, Ryosuke Okuta, Takuya Akiba, Yusuke Niitani, Toru Ogawa,\nShunta Saito, Shuji Suzuki, Kota Uenishi, Brian Vogel, and Hiroyuki\nYamazaki Vincent. 2019. “Chainer: A Deep Learning Framework for\nAccelerating the Research Cycle.” In Proceedings of the 25th\nACM SIGKDD International Conference on Knowledge Discovery &Amp;\nData Mining, 5:1–6. ACM. https://doi.org/10.1145/3292500.3330756.\n\n\nTramèr, Florian, Pascal Dupré, Gili Rusak, Giancarlo Pellegrino, and Dan\nBoneh. 2019. “AdVersarial: Perceptual Ad Blocking\nMeets Adversarial Machine Learning.” In Proceedings of the\n2019 ACM SIGSAC Conference on Computer and Communications Security,\n2005–21. ACM. https://doi.org/10.1145/3319535.3354222.\n\n\nTran, Cuong, Ferdinando Fioretto, Jung-Eun Kim, and Rakshit Naidu. 2022.\n“Pruning Has a Disparate Impact on Model Accuracy.” Adv\nNeural Inf Process Syst 35: 17652–64.\n\n\nTsai, Min-Jen, Ping-Yi Lin, and Ming-En Lee. 2023. “Adversarial\nAttacks on Medical Image Classification.” Cancers 15\n(17): 4228. https://doi.org/10.3390/cancers15174228.\n\n\nTsai, Timothy, Siva Kumar Sastry Hari, Michael Sullivan, Oreste Villa,\nand Stephen W. Keckler. 2021. “NVBitFI:\nDynamic Fault Injection for GPUs.” In\n2021 51st Annual IEEE/IFIP International Conference on Dependable\nSystems and Networks (DSN), 284–91. IEEE; IEEE. https://doi.org/10.1109/dsn48987.2021.00041.\n\n\nUddin, Mueen, and Azizah Abdul Rahman. 2012. “Energy Efficiency\nand Low Carbon Enabler Green IT Framework for Data Centers\nConsidering Green Metrics.” Renewable Sustainable Energy\nRev. 16 (6): 4078–94. https://doi.org/10.1016/j.rser.2012.03.014.\n\n\nUn, and World Economic Forum. 2019. A New Circular Vision for\nElectronics, Time for a Global Reboot. PACE - Platform for\nAccelerating the Circular Economy. https://www3.weforum.org/docs/WEF\\_A\\_New\\_Circular\\_Vision\\_for\\_Electronics.pdf.\n\n\nValenzuela, Christine L, and Pearl Y Wang. 2000. “A Genetic\nAlgorithm for VLSI Floorplanning.” In Parallel\nProblem Solving from Nature PPSN VI: 6th International Conference Paris,\nFrance, September 1820, 2000 Proceedings 6, 671–80.\nSpringer.\n\n\nVan Noorden, Richard. 2016. “ArXiv Preprint Server\nPlans Multimillion-Dollar Overhaul.” Nature 534 (7609):\n602–2. https://doi.org/10.1038/534602a.\n\n\nVangal, Sriram, Somnath Paul, Steven Hsu, Amit Agarwal, Saurabh Kumar,\nRam Krishnamurthy, Harish Krishnamurthy, James Tschanz, Vivek De, and\nChris H. Kim. 2021. “Wide-Range Many-Core SoC Design\nin Scaled CMOS: Challenges and\nOpportunities.” IEEE Trans. Very Large Scale Integr. VLSI\nSyst. 29 (5): 843–56. https://doi.org/10.1109/tvlsi.2021.3061649.\n\n\nVaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion\nJones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017.\n“Attention Is All You Need.” Adv Neural Inf Process\nSyst 30.\n\n\n“Vector-Borne Diseases.” n.d.\nhttps://www.who.int/news-room/fact-sheets/detail/vector-borne-diseases.\n\n\nVelazco, Raoul, Gilles Foucard, and Paul Peronnard. 2010.\n“Combining Results of Accelerated Radiation Tests and Fault\nInjections to Predict the Error Rate of an Application Implemented in\nSRAM-Based FPGAs.” IEEE Trans.\nNucl. Sci. 57 (6): 3500–3505. https://doi.org/10.1109/tns.2010.2087355.\n\n\nVerma, Naveen, Hongyang Jia, Hossein Valavi, Yinqi Tang, Murat Ozatay,\nLung-Yen Chen, Bonan Zhang, and Peter Deaville. 2019. “In-Memory\nComputing: Advances and Prospects.” IEEE\nSolid-State Circuits Mag. 11 (3): 43–55. https://doi.org/10.1109/mssc.2019.2922889.\n\n\nVerma, Team Dual_Boot: Swapnil. 2022. “Elephant\nAI.” Hackster.io. https://www.hackster.io/dual\\_boot/elephant-ai-ba71e9.\n\n\nVinuesa, Ricardo, Hossein Azizpour, Iolanda Leite, Madeline Balaam,\nVirginia Dignum, Sami Domisch, Anna Felländer, Simone Daniela Langhans,\nMax Tegmark, and Francesco Fuso Nerini. 2020. “The Role of\nArtificial Intelligence in Achieving the Sustainable Development\nGoals.” Nat. Commun. 11 (1): 1–10. https://doi.org/10.1038/s41467-019-14108-y.\n\n\nVivet, Pascal, Eric Guthmuller, Yvain Thonnart, Gael Pillonnet, Cesar\nFuguet, Ivan Miro-Panades, Guillaume Moritz, et al. 2021.\n“IntAct: A 96-Core Processor with Six\nChiplets 3D-Stacked on an Active Interposer with\nDistributed Interconnects and Integrated Power Management.”\nIEEE J. Solid-State Circuits 56 (1): 79–97. https://doi.org/10.1109/jssc.2020.3036341.\n\n\nWachter, Sandra, Brent Mittelstadt, and Chris Russell. 2017.\n“Counterfactual Explanations Without Opening the Black Box:\nAutomated Decisions and the GDPR.”\nSSRN Electronic Journal 31: 841. https://doi.org/10.2139/ssrn.3063289.\n\n\nWald, Peter H., and Jeffrey R. Jones. 1987. “Semiconductor\nManufacturing: An Introduction to Processes and\nHazards.” Am. J. Ind. Med. 11 (2): 203–21. https://doi.org/10.1002/ajim.4700110209.\n\n\nWan, Zishen, Aqeel Anwar, Yu-Shun Hsiao, Tianyu Jia, Vijay Janapa Reddi,\nand Arijit Raychowdhury. 2021. “Analyzing and Improving Fault\nTolerance of Learning-Based Navigation Systems.” In 2021 58th\nACM/IEEE Design Automation Conference (DAC), 841–46. IEEE; IEEE. https://doi.org/10.1109/dac18074.2021.9586116.\n\n\nWan, Zishen, Yiming Gan, Bo Yu, S Liu, A Raychowdhury, and Y Zhu. 2023.\n“Vpp: The Vulnerability-Proportional Protection\nParadigm Towards Reliable Autonomous Machines.” In\nProceedings of the 5th International Workshop on Domain Specific\nSystem Architecture (DOSSA), 1–6.\n\n\nWang, LingFeng, and YaQing Zhan. 2019a. “A Conceptual Peer Review\nModel for arXiv and Other Preprint\nDatabases.” Learn. Publ. 32 (3): 213–19. https://doi.org/10.1002/leap.1229.\n\n\n———. 2019b. “A Conceptual Peer Review Model for arXiv and Other Preprint Databases.”\nLearn. Publ. 32 (3): 213–19. https://doi.org/10.1002/leap.1229.\n\n\nWang, Tianzhe, Kuan Wang, Han Cai, Ji Lin, Zhijian Liu, Hanrui Wang,\nYujun Lin, and Song Han. 2020. “APQ:\nJoint Search for Network Architecture, Pruning and\nQuantization Policy.” In 2020 IEEE/CVF Conference on Computer\nVision and Pattern Recognition (CVPR), 2075–84. IEEE. https://doi.org/10.1109/cvpr42600.2020.00215.\n\n\nWarden, Pete. 2018. “Speech Commands: A Dataset for\nLimited-Vocabulary Speech Recognition.” arXiv Preprint\narXiv:1804.03209.\n\n\nWarden, Pete, and Daniel Situnayake. 2019. Tinyml:\nMachine Learning with Tensorflow Lite on Arduino and\nUltra-Low-Power Microcontrollers. O’Reilly Media.\n\n\nWeik, Martin H. 1955. A Survey of Domestic Electronic Digital\nComputing Systems. Ballistic Research Laboratories.\n\n\nWeiser, Mark. 1991. “The Computer for the 21st Century.”\nSci. Am. 265 (3): 94–104. https://doi.org/10.1038/scientificamerican0991-94.\n\n\nWess, Matthias, Matvey Ivanov, Christoph Unger, and Anvesh Nookala.\n2020. “ANNETTE: Accurate Neural Network\nExecution Time Estimation with Stacked Models.” IEEE. https://doi.org/10.1109/ACCESS.2020.3047259.\n\n\nWiener, Norbert. 1960. “Some Moral and Technical Consequences of\nAutomation: As Machines Learn They May Develop Unforeseen Strategies at\nRates That Baffle Their Programmers.” Science 131\n(3410): 1355–58. https://doi.org/10.1126/science.131.3410.1355.\n\n\nWilkening, Mark, Vilas Sridharan, Si Li, Fritz Previlon, Sudhanva\nGurumurthi, and David R. Kaeli. 2014. “Calculating Architectural\nVulnerability Factors for Spatial Multi-Bit Transient Faults.” In\n2014 47th Annual IEEE/ACM International Symposium on\nMicroarchitecture, 293–305. IEEE; IEEE. https://doi.org/10.1109/micro.2014.15.\n\n\nWinkler, Harald, Franck Lecocq, Hans Lofgren, Maria Virginia Vilariño,\nSivan Kartha, and Joana Portugal-Pereira. 2022. “Examples of\nShifting Development Pathways: Lessons on How to Enable\nBroader, Deeper, and Faster Climate Action.” Climate\nAction 1 (1). https://doi.org/10.1007/s44168-022-00026-1.\n\n\nWong, H.-S. Philip, Heng-Yuan Lee, Shimeng Yu, Yu-Sheng Chen, Yi Wu,\nPang-Shiu Chen, Byoungil Lee, Frederick T. Chen, and Ming-Jinn Tsai.\n2012. “MetalOxide\nRRAM.” Proc. IEEE 100 (6): 1951–70. https://doi.org/10.1109/jproc.2012.2190369.\n\n\nWu, Bichen, Kurt Keutzer, Xiaoliang Dai, Peizhao Zhang, Yanghan Wang,\nFei Sun, Yiming Wu, Yuandong Tian, Peter Vajda, and Yangqing Jia. 2019.\n“FBNet: Hardware-aware\nEfficient ConvNet Design via Differentiable Neural\nArchitecture Search.” In 2019 IEEE/CVF Conference on Computer\nVision and Pattern Recognition (CVPR), 10734–42. IEEE. https://doi.org/10.1109/cvpr.2019.01099.\n\n\nWu, Carole-Jean, Ramya Raghavendra, Udit Gupta, Bilge Acun, Newsha\nArdalani, Kiwan Maeng, Gloria Chang, et al. 2022. “Sustainable Ai:\nEnvironmental Implications, Challenges and\nOpportunities.” Proceedings of Machine Learning and\nSystems 4: 795–813.\n\n\nWu, Zhang Judd, and Micikevicius Isaev. 2020. “Integer\nQuantization for Deep Learning Inference: Principles and\nEmpirical Evaluation).” ArXiv Preprint. https://arxiv.org/abs/2004.09602.\n\n\nXiao, Seznec Lin, Demouth Wu, and Han. 2022.\n“SmoothQuant: Accurate and Efficient\nPost-Training Quantization for Large Language Models.” ArXiv\nPreprint. https://arxiv.org/abs/2211.10438.\n\n\nXie, Cihang, Mingxing Tan, Boqing Gong, Jiang Wang, Alan L. Yuille, and\nQuoc V. Le. 2020. “Adversarial Examples Improve Image\nRecognition.” In 2020 IEEE/CVF Conference on Computer Vision\nand Pattern Recognition (CVPR), 816–25. IEEE. https://doi.org/10.1109/cvpr42600.2020.00090.\n\n\nXie, Saining, Ross Girshick, Piotr Dollar, Zhuowen Tu, and Kaiming He.\n2017. “Aggregated Residual Transformations for Deep Neural\nNetworks.” In 2017 IEEE Conference on Computer Vision and\nPattern Recognition (CVPR), 1492–1500. IEEE. https://doi.org/10.1109/cvpr.2017.634.\n\n\nXinyu, Chen. n.d.\n\n\nXiong, Siyu, Guoqing Wu, Xitian Fan, Xuan Feng, Zhongcheng Huang, Wei\nCao, Xuegong Zhou, et al. 2021. “MRI-Based Brain\nTumor Segmentation Using FPGA-Accelerated Neural\nNetwork.” BMC Bioinf. 22 (1): 421. https://doi.org/10.1186/s12859-021-04347-6.\n\n\nXiu, Liming. 2019. “Time Moore: Exploiting Moore’s Law from the Perspective of Time.”\nIEEE Solid-State Circuits Mag. 11 (1): 39–55. https://doi.org/10.1109/mssc.2018.2882285.\n\n\nXu, Chen, Jianqiang Yao, Zhouchen Lin, Wenwu Ou, Yuanbin Cao, Zhirong\nWang, and Hongbin Zha. 2018. “Alternating Multi-Bit Quantization\nfor Recurrent Neural Networks.” In 6th International\nConference on Learning Representations, ICLR 2018, Vancouver, BC,\nCanada, April 30 - May 3, 2018, Conference Track Proceedings.\nOpenReview.net. https://openreview.net/forum?id=S19dR9x0b.\n\n\nXu, Hu, Saining Xie, Xiaoqing Ellen Tan, Po-Yao Huang, Russell Howes,\nVasu Sharma, Shang-Wen Li, Gargi Ghosh, Luke Zettlemoyer, and Christoph\nFeichtenhofer. 2023. “Demystifying CLIP Data.”\nArXiv Preprint abs/2309.16671. https://arxiv.org/abs/2309.16671.\n\n\nXu, Ying, Xu Zhong, Antonio Jimeno Yepes, and Jey Han Lau. 2021.\n“Grey-Box Adversarial Attack and Defence for\nSentiment Classification.” arXiv Preprint\narXiv:2103.11576.\n\n\nXu, Zheng, Yanxiang Zhang, Galen Andrew, Christopher A Choquette-Choo,\nPeter Kairouz, H Brendan McMahan, Jesse Rosenstock, and Yuanbo Zhang.\n2023. “Federated Learning of Gboard Language Models with\nDifferential Privacy.” ArXiv Preprint abs/2305.18465. https://arxiv.org/abs/2305.18465.\n\n\nYang, Tien-Ju, Yonghui Xiao, Giovanni Motta, Françoise Beaufays, Rajiv\nMathews, and Mingqing Chen. 2023. “Online Model Compression for\nFederated Learning with Large Models.” In ICASSP 2023 - 2023\nIEEE International Conference on Acoustics, Speech and Signal Processing\n(ICASSP), 1–5. IEEE; IEEE. https://doi.org/10.1109/icassp49357.2023.10097124.\n\n\nYao, Zhewei, Zhen Dong, Zhangcheng Zheng, Amir Gholami, Jiali Yu, Eric\nTan, Leyuan Wang, et al. 2021. “Hawq-V3: Dyadic\nNeural Network Quantization.” In International Conference on\nMachine Learning, 11875–86. PMLR.\n\n\nYe, Linfeng, and Shayan Mohajer Hamidi. 2021. “Thundernna:\nA White Box Adversarial Attack.” arXiv Preprint\narXiv:2111.12305.\n\n\nYeh, Y. C. 1996. “Triple-Triple Redundant 777 Primary Flight\nComputer.” In 1996 IEEE Aerospace Applications Conference.\nProceedings, 1:293–307. IEEE; IEEE. https://doi.org/10.1109/aero.1996.495891.\n\n\nYik, Jason, Soikat Hasan Ahmed, Zergham Ahmed, Brian Anderson, Andreas\nG. Andreou, Chiara Bartolozzi, Arindam Basu, et al. 2023.\n“NeuroBench: Advancing Neuromorphic\nComputing Through Collaborative, Fair and Representative\nBenchmarking.” https://arxiv.org/abs/2304.04640.\n\n\nYou, Jie, Jae-Won Chung, and Mosharaf Chowdhury. 2023. “Zeus:\nUnderstanding and Optimizing GPU Energy\nConsumption of DNN Training.” In 20th USENIX\nSymposium on Networked Systems Design and Implementation (NSDI 23),\n119–39. Boston, MA: USENIX Association. https://www.usenix.org/conference/nsdi23/presentation/you.\n\n\nYou, Yang, Zhao Zhang, Cho-Jui Hsieh, James Demmel, and Kurt Keutzer.\n2017. “ImageNet Training in Minutes,” September. http://arxiv.org/abs/1709.05011v10.\n\n\nYoung, Tom, Devamanyu Hazarika, Soujanya Poria, and Erik Cambria. 2018.\n“Recent Trends in Deep Learning Based Natural Language Processing\n[Review Article].” IEEE Comput. Intell.\nMag. 13 (3): 55–75. https://doi.org/10.1109/mci.2018.2840738.\n\n\nYu, Yuan, Martı́n Abadi, Paul Barham, Eugene Brevdo, Mike Burrows, Andy\nDavis, Jeff Dean, et al. 2018. “Dynamic Control Flow in\nLarge-Scale Machine Learning.” In Proceedings of the\nThirteenth EuroSys Conference, 265–83. ACM. https://doi.org/10.1145/3190508.3190551.\n\n\nZafrir, Ofir, Guy Boudoukh, Peter Izsak, and Moshe Wasserblat. 2019.\n“Q8BERT: Quantized 8Bit\nBERT.” In 2019 Fifth Workshop on Energy\nEfficient Machine Learning and Cognitive Computing - NeurIPS Edition\n(EMC2-NIPS), 36–39. IEEE; IEEE. https://doi.org/10.1109/emc2-nips53020.2019.00016.\n\n\nZeiler, Matthew D. 2012. “ADADELTA: An Adaptive Learning Rate\nMethod,” December, 119–49. https://doi.org/10.1002/9781118266502.ch6.\n\n\nZennaro, Marco, Brian Plancher, and V Janapa Reddi. 2022.\n“TinyML: Applied AI for\nDevelopment.” In The UN 7th Multi-Stakeholder Forum on\nScience, Technology and Innovation for the Sustainable Development\nGoals, 2022–05.\n\n\nZhang, Chen, Peng Li, Guangyu Sun, Yijin Guan, Bingjun Xiao, and Jason\nOptimizing Cong. 2015. “FPGA-Based Accelerator Design\nfor Deep Convolutional Neural Networks Proceedings of the 2015\nACM.” In SIGDA International Symposium on\nField-Programmable Gate Arrays-FPGA, 15:161–70.\n\n\nZhang, Dan, Safeen Huda, Ebrahim Songhori, Kartik Prabhu, Quoc Le, Anna\nGoldie, and Azalia Mirhoseini. 2022. “A Full-Stack Search\nTechnique for Domain Optimized Deep Learning Accelerators.” In\nProceedings of the 27th ACM International Conference on\nArchitectural Support for Programming Languages and Operating\nSystems, 27–42. ASPLOS ’22. New York, NY, USA: ACM. https://doi.org/10.1145/3503222.3507767.\n\n\nZhang, Dongxia, Xiaoqing Han, and Chunyu Deng. 2018. “Review on\nthe Research and Practice of Deep Learning and Reinforcement Learning in\nSmart Grids.” CSEE Journal of Power and Energy Systems 4\n(3): 362–70. https://doi.org/10.17775/cseejpes.2018.00520.\n\n\nZhang, Hongyu. 2008. “On the Distribution of Software\nFaults.” IEEE Trans. Software Eng. 34 (2): 301–2. https://doi.org/10.1109/tse.2007.70771.\n\n\nZhang, Jeff Jun, Tianyu Gu, Kanad Basu, and Siddharth Garg. 2018.\n“Analyzing and Mitigating the Impact of Permanent Faults on a\nSystolic Array Based Neural Network Accelerator.” In 2018\nIEEE 36th VLSI Test Symposium (VTS), 1–6. IEEE; IEEE. https://doi.org/10.1109/vts.2018.8368656.\n\n\nZhang, Jeff, Kartheek Rangineni, Zahra Ghodsi, and Siddharth Garg. 2018.\n“ThUnderVolt: Enabling Aggressive\nVoltage Underscaling and Timing Error Resilience for Energy Efficient\nDeep Learning Accelerators.” In 2018 55th ACM/ESDA/IEEE\nDesign Automation Conference (DAC), 1–6. IEEE. https://doi.org/10.1109/dac.2018.8465918.\n\n\nZhang, Li Lyna, Yuqing Yang, Yuhang Jiang, Wenwu Zhu, and Yunxin Liu.\n2020. “Fast Hardware-Aware Neural Architecture Search.” In\n2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition\nWorkshops (CVPRW). IEEE. https://doi.org/10.1109/cvprw50498.2020.00354.\n\n\nZhang, Qingxue, Dian Zhou, and Xuan Zeng. 2017. “Highly Wearable\nCuff-Less Blood Pressure and Heart Rate Monitoring with Single-Arm\nElectrocardiogram and Photoplethysmogram Signals.” BioMedical\nEngineering OnLine 16 (1): 23. https://doi.org/10.1186/s12938-017-0317-z.\n\n\nZhang, Tunhou, Hsin-Pai Cheng, Zhenwen Li, Feng Yan, Chengyu Huang, Hai\nHelen Li, and Yiran Chen. 2020. “AutoShrink:\nA Topology-Aware NAS for Discovering Efficient\nNeural Architecture.” In The Thirty-Fourth AAAI Conference on\nArtificial Intelligence, AAAI 2020, the Thirty-Second Innovative\nApplications of Artificial Intelligence Conference, IAAI 2020, the Tenth\nAAAI Symposium on Educational Advances in Artificial Intelligence, EAAI\n2020, New York, NY, USA, February 7-12, 2020, 6829–36. AAAI Press.\nhttps://aaai.org/ojs/index.php/AAAI/article/view/6163.\n\n\nZhao, Mark, and G. Edward Suh. 2018. “FPGA-Based\nRemote Power Side-Channel Attacks.” In 2018 IEEE Symposium on\nSecurity and Privacy (SP), 229–44. IEEE; IEEE. https://doi.org/10.1109/sp.2018.00049.\n\n\nZhao, Yue, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas\nChandra. 2018. “Federated Learning with Non-Iid Data.”\nArXiv Preprint abs/1806.00582. https://arxiv.org/abs/1806.00582.\n\n\nZhou, Bolei, Yiyou Sun, David Bau, and Antonio Torralba. 2018.\n“Interpretable Basis Decomposition for Visual Explanation.”\nIn Proceedings of the European Conference on Computer Vision\n(ECCV), 119–34.\n\n\nZhou, Chuteng, Fernando Garcia Redondo, Julian Büchel, Irem Boybat,\nXavier Timoneda Comas, S. R. Nandakumar, Shidhartha Das, Abu Sebastian,\nManuel Le Gallo, and Paul N. Whatmough. 2021.\n“AnalogNets: Ml-hw\nCo-Design of Noise-Robust TinyML Models and Always-on\nAnalog Compute-in-Memory Accelerator.” https://arxiv.org/abs/2111.06503.\n\n\nZhou, Peng, Xintong Han, Vlad I. Morariu, and Larry S. Davis. 2018.\n“Learning Rich Features for Image Manipulation Detection.”\nIn 2018 IEEE/CVF Conference on Computer Vision and Pattern\nRecognition, 1053–61. IEEE. https://doi.org/10.1109/cvpr.2018.00116.\n\n\nZhu, Hongyu, Mohamed Akrout, Bojian Zheng, Andrew Pelegris, Anand\nJayarajan, Amar Phanishayee, Bianca Schroeder, and Gennady Pekhimenko.\n2018. “Benchmarking and Analyzing Deep Neural Network\nTraining.” In 2018 IEEE International Symposium on Workload\nCharacterization (IISWC), 88–100. IEEE; IEEE. https://doi.org/10.1109/iiswc.2018.8573476.\n\n\nZhu, Ligeng, Lanxiang Hu, Ji Lin, Wei-Ming Chen, Wei-Chen Wang, Chuang\nGan, and Song Han. 2023. “PockEngine:\nSparse and Efficient Fine-Tuning in a Pocket.” In\n56th Annual IEEE/ACM International Symposium on\nMicroarchitecture. ACM. https://doi.org/10.1145/3613424.3614307.\n\n\nZhuang, Fuzhen, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, Hengshu\nZhu, Hui Xiong, and Qing He. 2021. “A Comprehensive Survey on\nTransfer Learning.” Proc. IEEE 109 (1): 43–76. https://doi.org/10.1109/jproc.2020.3004555.\n\n\nZoph, Barret, and Quoc V. Le. 2016. “Neural Architecture Search\nwith Reinforcement Learning,” November, 367–92. https://doi.org/10.1002/9781394217519.ch17.",
+    "text": "Abadi, Martin, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya\nMironov, Kunal Talwar, and Li Zhang. 2016. “Deep Learning with\nDifferential Privacy.” In Proceedings of the 2016 ACM SIGSAC\nConference on Computer and Communications Security, 308–18. CCS\n’16. New York, NY, USA: ACM. https://doi.org/10.1145/2976749.2978318.\n\n\nAbdelkader, Ahmed, Michael J. Curry, Liam Fowl, Tom Goldstein, Avi\nSchwarzschild, Manli Shu, Christoph Studer, and Chen Zhu. 2020.\n“Headless Horseman: Adversarial Attacks on Transfer\nLearning Models.” In ICASSP 2020 - 2020 IEEE International\nConference on Acoustics, Speech and Signal Processing (ICASSP),\n3087–91. IEEE. https://doi.org/10.1109/icassp40776.2020.9053181.\n\n\nAddepalli, Sravanti, B. S. Vivek, Arya Baburaj, Gaurang Sriramanan, and\nR. Venkatesh Babu. 2020. “Towards Achieving Adversarial Robustness\nby Enforcing Feature Consistency Across Bit Planes.” In 2020\nIEEE/CVF Conference on Computer Vision and Pattern Recognition\n(CVPR), 1020–29. IEEE. https://doi.org/10.1109/cvpr42600.2020.00110.\n\n\nAdolf, Robert, Saketh Rama, Brandon Reagen, Gu-yeon Wei, and David\nBrooks. 2016. “Fathom: Reference Workloads for Modern\nDeep Learning Methods.” In 2016 IEEE International Symposium\non Workload Characterization (IISWC), 1–10. IEEE; IEEE. https://doi.org/10.1109/iiswc.2016.7581275.\n\n\nAgarwal, Alekh, Alina Beygelzimer, Miroslav Dudı́k, John Langford, and\nHanna M. Wallach. 2018. “A Reductions Approach to Fair\nClassification.” In Proceedings of the 35th International\nConference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm,\nSweden, July 10-15, 2018, edited by Jennifer G. Dy and Andreas\nKrause, 80:60–69. Proceedings of Machine Learning Research. PMLR. http://proceedings.mlr.press/v80/agarwal18a.html.\n\n\nAgnesina, Anthony, Puranjay Rajvanshi, Tian Yang, Geraldo Pradipta,\nAustin Jiao, Ben Keller, Brucek Khailany, and Haoxing Ren. 2023.\n“AutoDMP: Automated DREAMPlace-Based Macro\nPlacement.” In Proceedings of the 2023 International\nSymposium on Physical Design, 149–57. ACM. https://doi.org/10.1145/3569052.3578923.\n\n\nAgrawal, Dakshi, Selcuk Baktir, Deniz Karakoyunlu, Pankaj Rohatgi, and\nBerk Sunar. 2007. “Trojan Detection Using\nIC Fingerprinting.” In 2007 IEEE Symposium on\nSecurity and Privacy (SP ’07), 29–45. Springer; IEEE. https://doi.org/10.1109/sp.2007.36.\n\n\nAhmadilivani, Mohammad Hasan, Mahdi Taheri, Jaan Raik, Masoud\nDaneshtalab, and Maksim Jenihhin. 2024. “A Systematic Literature\nReview on Hardware Reliability Assessment Methods for Deep Neural\nNetworks.” ACM Comput. Surv. 56 (6): 1–39. https://doi.org/10.1145/3638242.\n\n\nAledhari, Mohammed, Rehma Razzak, Reza M. Parizi, and Fahad Saeed. 2020.\n“Federated Learning: A Survey on Enabling\nTechnologies, Protocols, and Applications.” #IEEE_O_ACC#\n8: 140699–725. https://doi.org/10.1109/access.2020.3013541.\n\n\nAlghamdi, Wael, Hsiang Hsu, Haewon Jeong, Hao Wang, Peter Michalak,\nShahab Asoodeh, and Flavio Calmon. 2022. “Beyond Adult and\nCOMPAS: Fair Multi-Class Prediction via\nInformation Projection.” Adv. Neur. In. 35: 38747–60.\n\n\nAltayeb, Moez, Marco Zennaro, and Marcelo Rovai. 2022.\n“Classifying Mosquito Wingbeat Sound Using\nTinyML.” In Proceedings of the 2022 ACM\nConference on Information Technology for Social Good, 132–37. ACM.\nhttps://doi.org/10.1145/3524458.3547258.\n\n\nAmiel, Frederic, Christophe Clavier, and Michael Tunstall. 2006.\n“Fault Analysis of DPA-Resistant Algorithms.”\nIn International Workshop on Fault Diagnosis and Tolerance in\nCryptography, 223–36. Springer.\n\n\nAnsel, Jason, Edward Yang, Horace He, Natalia Gimelshein, Animesh Jain,\nMichael Voznesensky, Bin Bao, et al. 2024. “PyTorch\n2: Faster Machine Learning Through Dynamic Python Bytecode\nTransformation and Graph Compilation.” In Proceedings of the\n29th ACM International Conference on Architectural Support for\nProgramming Languages and Operating Systems, Volume 2, edited by\nHanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence\nd’Alché-Buc, Emily B. Fox, and Roman Garnett, 8024–35. ACM. https://doi.org/10.1145/3620665.3640366.\n\n\nAnthony, Lasse F. Wolff, Benjamin Kanding, and Raghavendra Selvan. 2020.\nICML Workshop on Challenges in Deploying and monitoring Machine Learning\nSystems.\n\n\nAntol, Stanislaw, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv\nBatra, C. Lawrence Zitnick, and Devi Parikh. 2015.\n“VQA: Visual Question Answering.”\nIn 2015 IEEE International Conference on Computer Vision\n(ICCV), 2425–33. IEEE. https://doi.org/10.1109/iccv.2015.279.\n\n\nAntonakakis, Manos, Tim April, Michael Bailey, Matt Bernhard, Elie\nBursztein, Jaime Cochran, Zakir Durumeric, et al. 2017.\n“Understanding the Mirai Botnet.” In 26th USENIX\nSecurity Symposium (USENIX Security 17), 1093–1110.\n\n\nArdila, Rosana, Megan Branson, Kelly Davis, Michael Kohler, Josh Meyer,\nMichael Henretty, Reuben Morais, Lindsay Saunders, Francis Tyers, and\nGregor Weber. 2020. “Common Voice: A\nMassively-Multilingual Speech Corpus.” In Proceedings of the\nTwelfth Language Resources and Evaluation Conference, 4218–22.\nMarseille, France: European Language Resources Association. https://aclanthology.org/2020.lrec-1.520.\n\n\nArifeen, Tooba, Abdus Sami Hassan, and Jeong-A Lee. 2020.\n“Approximate Triple Modular Redundancy: A\nSurvey.” #IEEE_O_ACC# 8: 139851–67. https://doi.org/10.1109/access.2020.3012673.\n\n\nAsonov, D., and R. Agrawal. 2004. “Keyboard Acoustic\nEmanations.” In IEEE Symposium on Security and Privacy, 2004.\nProceedings. 2004, 3–11. IEEE; IEEE. https://doi.org/10.1109/secpri.2004.1301311.\n\n\nAteniese, Giuseppe, Luigi V. Mancini, Angelo Spognardi, Antonio Villani,\nDomenico Vitali, and Giovanni Felici. 2015. “Hacking Smart\nMachines with Smarter Ones: How to Extract Meaningful Data\nfrom Machine Learning Classifiers.” Int. J. Secur. Netw.\n10 (3): 137. https://doi.org/10.1504/ijsn.2015.071829.\n\n\nAttia, Zachi I., Alan Sugrue, Samuel J. Asirvatham, Michael J. Ackerman,\nSuraj Kapa, Paul A. Friedman, and Peter A. Noseworthy. 2018.\n“Noninvasive Assessment of Dofetilide Plasma Concentration Using a\nDeep Learning (Neural Network) Analysis of the Surface\nElectrocardiogram: A Proof of Concept Study.”\nPLoS One 13 (8): e0201059. https://doi.org/10.1371/journal.pone.0201059.\n\n\nAygun, Sercan, Ece Olcay Gunes, and Christophe De Vleeschouwer. 2021.\n“Efficient and Robust Bitstream Processing in Binarised Neural\nNetworks.” Electron. Lett. 57 (5): 219–22. https://doi.org/10.1049/ell2.12045.\n\n\nBai, Tao, Jinqi Luo, Jun Zhao, Bihan Wen, and Qian Wang. 2021.\n“Recent Advances in Adversarial Training for Adversarial\nRobustness.” arXiv Preprint arXiv:2102.01356.\n\n\nBains, Sunny. 2020. “The Business of Building Brains.”\nNature Electronics 3 (7): 348–51. https://doi.org/10.1038/s41928-020-0449-1.\n\n\nBamoumen, Hatim, Anas Temouden, Nabil Benamar, and Yousra Chtouki. 2022.\n“How TinyML Can Be Leveraged to Solve Environmental\nProblems: A Survey.” In 2022 International\nConference on Innovation and Intelligence for Informatics, Computing,\nand Technologies (3ICT), 338–43. IEEE; IEEE. https://doi.org/10.1109/3ict56508.2022.9990661.\n\n\nBank, Dor, Noam Koenigstein, and Raja Giryes. 2023.\n“Autoencoders.” Machine Learning for Data Science\nHandbook: Data Mining and Knowledge Discovery Handbook, 353–74.\n\n\nBannon, Pete, Ganesh Venkataramanan, Debjit Das Sarma, and Emil Talpes.\n2019. “Computer and Redundancy Solution for the Full Self-Driving\nComputer.” In 2019 IEEE Hot Chips 31 Symposium (HCS),\n1–22. IEEE Computer Society; IEEE. https://doi.org/10.1109/hotchips.2019.8875645.\n\n\nBarenghi, Alessandro, Guido M. Bertoni, Luca Breveglieri, Mauro\nPellicioli, and Gerardo Pelosi. 2010. “Low Voltage Fault Attacks\nto AES.” In 2010 IEEE International Symposium on\nHardware-Oriented Security and Trust (HOST), 7–12. IEEE; IEEE. https://doi.org/10.1109/hst.2010.5513121.\n\n\nBarroso, Luiz André, Urs Hölzle, and Parthasarathy Ranganathan. 2019.\nThe Datacenter as a Computer: Designing Warehouse-Scale\nMachines. Springer International Publishing. https://doi.org/10.1007/978-3-031-01761-2.\n\n\nBau, David, Bolei Zhou, Aditya Khosla, Aude Oliva, and Antonio Torralba.\n2017. “Network Dissection: Quantifying\nInterpretability of Deep Visual Representations.” In 2017\nIEEE Conference on Computer Vision and Pattern Recognition (CVPR),\n3319–27. IEEE. https://doi.org/10.1109/cvpr.2017.354.\n\n\nBeaton, Albert E., and John W. Tukey. 1974. “The Fitting of Power\nSeries, Meaning Polynomials, Illustrated on Band-Spectroscopic\nData.” Technometrics 16 (2): 147. https://doi.org/10.2307/1267936.\n\n\nBeck, Nathaniel, and Simon Jackman. 1998. “Beyond Linearity by\nDefault: Generalized Additive Models.” Am. J.\nPolit. Sci. 42 (2): 596. https://doi.org/10.2307/2991772.\n\n\nBender, Emily M., and Batya Friedman. 2018. “Data Statements for\nNatural Language Processing: Toward Mitigating System Bias\nand Enabling Better Science.” Transactions of the Association\nfor Computational Linguistics 6 (December): 587–604. https://doi.org/10.1162/tacl_a_00041.\n\n\nBerger, Vance W, and YanYan Zhou. 2014.\n“Kolmogorovsmirnov Test:\nOverview.” Wiley Statsref: Statistics Reference\nOnline.\n\n\nBeyer, Lucas, Olivier J Hénaff, Alexander Kolesnikov, Xiaohua Zhai, and\nAäron van den Oord. 2020. “Are We Done with Imagenet?”\nArXiv Preprint abs/2006.07159. https://arxiv.org/abs/2006.07159.\n\n\nBhagoji, Arjun Nitin, Warren He, Bo Li, and Dawn Song. 2018.\n“Practical Black-Box Attacks on Deep Neural Networks Using\nEfficient Query Mechanisms.” In Proceedings of the European\nConference on Computer Vision (ECCV), 154–69.\n\n\nBhardwaj, Kshitij, Marton Havasi, Yuan Yao, David M. Brooks, José Miguel\nHernández-Lobato, and Gu-Yeon Wei. 2020. “A Comprehensive\nMethodology to Determine Optimal Coherence Interfaces for\nMany-Accelerator SoCs.” In Proceedings of the\nACM/IEEE International Symposium on Low Power Electronics and\nDesign, 145–50. ACM. https://doi.org/10.1145/3370748.3406564.\n\n\nBianco, Simone, Remi Cadene, Luigi Celona, and Paolo Napoletano. 2018.\n“Benchmark Analysis of Representative Deep Neural Network\nArchitectures.” IEEE Access 6: 64270–77.\n\n\nBiega, Asia J., Peter Potash, Hal Daumé, Fernando Diaz, and Michèle\nFinck. 2020. “Operationalizing the Legal Principle of Data\nMinimization for Personalization.” In Proceedings of the 43rd\nInternational ACM SIGIR Conference on Research and Development in\nInformation Retrieval, edited by Jimmy Huang, Yi Chang, Xueqi\nCheng, Jaap Kamps, Vanessa Murdock, Ji-Rong Wen, and Yiqun Liu, 399–408.\nACM. https://doi.org/10.1145/3397271.3401034.\n\n\nBiggio, Battista, Blaine Nelson, and Pavel Laskov. 2012.\n“Poisoning Attacks Against Support Vector Machines.” In\nProceedings of the 29th International Conference on Machine\nLearning, ICML 2012, Edinburgh, Scotland, UK, June 26 - July 1,\n2012. icml.cc / Omnipress. http://icml.cc/2012/papers/880.pdf.\n\n\nBiggs, John, James Myers, Jedrzej Kufel, Emre Ozer, Simon Craske, Antony\nSou, Catherine Ramsdale, Ken Williamson, Richard Price, and Scott White.\n2021. “A Natively Flexible 32-Bit Arm Microprocessor.”\nNature 595 (7868): 532–36. https://doi.org/10.1038/s41586-021-03625-w.\n\n\nBinkert, Nathan, Bradford Beckmann, Gabriel Black, Steven K. Reinhardt,\nAli Saidi, Arkaprava Basu, Joel Hestness, et al. 2011. “The Gem5\nSimulator.” ACM SIGARCH Computer Architecture News 39\n(2): 1–7. https://doi.org/10.1145/2024716.2024718.\n\n\nBohr, Adam, and Kaveh Memarzadeh. 2020. “The Rise of Artificial\nIntelligence in Healthcare Applications.” In Artificial\nIntelligence in Healthcare, 25–60. Elsevier. https://doi.org/10.1016/b978-0-12-818438-7.00002-2.\n\n\nBolchini, Cristiana, Luca Cassano, Antonio Miele, and Alessandro Toschi.\n2023. “Fast and Accurate Error Simulation for CNNs\nAgainst Soft Errors.” IEEE Trans. Comput. 72 (4):\n984–97. https://doi.org/10.1109/tc.2022.3184274.\n\n\nBondi, Elizabeth, Ashish Kapoor, Debadeepta Dey, James Piavis, Shital\nShah, Robert Hannaford, Arvind Iyer, Lucas Joppa, and Milind Tambe.\n2018. “Near Real-Time Detection of Poachers from Drones in\nAirSim.” In Proceedings of the Twenty-Seventh\nInternational Joint Conference on Artificial Intelligence, edited\nby Jérôme Lang, 5814–16. International Joint Conferences on Artificial\nIntelligence Organization. https://doi.org/10.24963/ijcai.2018/847.\n\n\nBourtoule, Lucas, Varun Chandrasekaran, Christopher A. Choquette-Choo,\nHengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas\nPapernot. 2021. “Machine Unlearning.” In 2021 IEEE\nSymposium on Security and Privacy (SP), 141–59. IEEE; IEEE. https://doi.org/10.1109/sp40001.2021.00019.\n\n\nBreier, Jakub, Xiaolu Hou, Dirmanto Jap, Lei Ma, Shivam Bhasin, and Yang\nLiu. 2018. “Deeplaser: Practical Fault Attack on Deep\nNeural Networks.” ArXiv Preprint abs/1806.05859. https://arxiv.org/abs/1806.05859.\n\n\nBrown, Tom B., Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan,\nPrafulla Dhariwal, Arvind Neelakantan, et al. 2020. “Language\nModels Are Few-Shot Learners.” In Advances in Neural\nInformation Processing Systems 33: Annual Conference on Neural\nInformation Processing Systems 2020, NeurIPS 2020, December 6-12, 2020,\nVirtual, edited by Hugo Larochelle, Marc’Aurelio Ranzato, Raia\nHadsell, Maria-Florina Balcan, and Hsuan-Tien Lin. https://proceedings.neurips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html.\n\n\nBuolamwini, Joy, and Timnit Gebru. 2018. “Gender Shades:\nIntersectional Accuracy Disparities in Commercial Gender\nClassification.” In Conference on Fairness, Accountability\nand Transparency, 77–91. PMLR.\n\n\nBurnet, David, and Richard Thomas. 1989. “Spycatcher:\nThe Commodification of Truth.” J. Law Soc.\n16 (2): 210. https://doi.org/10.2307/1410360.\n\n\nBurr, Geoffrey W., Matthew J. BrightSky, Abu Sebastian, Huai-Yu Cheng,\nJau-Yi Wu, Sangbum Kim, Norma E. Sosa, et al. 2016. “Recent\nProgress in Phase-Change?Pub _Newline ?Memory\nTechnology.” IEEE Journal on Emerging and Selected Topics in\nCircuits and Systems 6 (2): 146–62. https://doi.org/10.1109/jetcas.2016.2547718.\n\n\nBushnell, Michael L, and Vishwani D Agrawal. 2002. “Built-in\nSelf-Test.” Essentials of Electronic Testing for Digital,\nMemory and Mixed-Signal VLSI Circuits, 489–548.\n\n\nBuyya, Rajkumar, Anton Beloglazov, and Jemal Abawajy. 2010.\n“Energy-Efficient Management of Data Center Resources for Cloud\nComputing: A Vision, Architectural Elements, and Open\nChallenges.” https://arxiv.org/abs/1006.0308.\n\n\nCai, Carrie J., Emily Reif, Narayan Hegde, Jason Hipp, Been Kim, Daniel\nSmilkov, Martin Wattenberg, et al. 2019. “Human-Centered Tools for\nCoping with Imperfect Algorithms During Medical Decision-Making.”\nIn Proceedings of the 2019 CHI Conference on Human Factors in\nComputing Systems, edited by Jennifer G. Dy and Andreas Krause,\n80:2673–82. Proceedings of Machine Learning Research. ACM. https://doi.org/10.1145/3290605.3300234.\n\n\nCai, Han, Chuang Gan, Ligeng Zhu, and Song Han. 2020.\n“TinyTL: Reduce Memory, Not Parameters\nfor Efficient on-Device Learning.” In Advances in Neural\nInformation Processing Systems 33: Annual Conference on Neural\nInformation Processing Systems 2020, NeurIPS 2020, December 6-12, 2020,\nVirtual, edited by Hugo Larochelle, Marc’Aurelio Ranzato, Raia\nHadsell, Maria-Florina Balcan, and Hsuan-Tien Lin. https://proceedings.neurips.cc/paper/2020/hash/81f7acabd411274fcf65ce2070ed568a-Abstract.html.\n\n\nCai, Han, Ligeng Zhu, and Song Han. 2019.\n“ProxylessNAS: Direct Neural\nArchitecture Search on Target Task and Hardware.” In 7th\nInternational Conference on Learning Representations, ICLR 2019, New\nOrleans, LA, USA, May 6-9, 2019. OpenReview.net. https://openreview.net/forum?id=HylVB3AqYm.\n\n\nCalvo, Rafael A, Dorian Peters, Karina Vold, and Richard M Ryan. 2020.\n“Supporting Human Autonomy in AI Systems:\nA Framework for Ethical Enquiry.” Ethics of\nDigital Well-Being: A Multidisciplinary Approach, 31–54.\n\n\nCarlini, Nicholas, Pratyush Mishra, Tavish Vaidya, Yuankai Zhang, Micah\nSherr, Clay Shields, David Wagner, and Wenchao Zhou. 2016. “Hidden\nVoice Commands.” In 25th USENIX Security Symposium (USENIX\nSecurity 16), 513–30.\n\n\nCarta, Salvatore, Alessandro Sebastian Podda, Diego Reforgiato Recupero,\nand Roberto Saia. 2020. “A Local Feature Engineering Strategy to\nImprove Network Anomaly Detection.” Future Internet 12\n(10): 177. https://doi.org/10.3390/fi12100177.\n\n\nCavoukian, Ann. 2009. “Privacy by Design.” Office of\nthe Information and Privacy Commissioner.\n\n\nCenci, Marcelo Pilotto, Tatiana Scarazzato, Daniel Dotto Munchen, Paula\nCristina Dartora, Hugo Marcelo Veit, Andrea Moura Bernardes, and Pablo\nR. Dias. 2021. “Eco-Friendly\nElectronicsA Comprehensive Review.”\nAdv. Mater. Technol. 7 (2): 2001263. https://doi.org/10.1002/admt.202001263.\n\n\nChallenge, WEF Net-Zero. 2021. “The Supply Chain\nOpportunity.” In World Economic Forum: Geneva,\nSwitzerland.\n\n\nChandola, Varun, Arindam Banerjee, and Vipin Kumar. 2009. “Anomaly\nDetection: A Survey.” ACM Comput. Surv. 41 (3): 1–58. https://doi.org/10.1145/1541880.1541882.\n\n\nChapelle, O., B. Scholkopf, and A. Zien Eds. 2009.\n“Semi-Supervised Learning (Chapelle, O.\nEt Al., Eds.; 2006) [Book Reviews].” IEEE Trans.\nNeural Networks 20 (3): 542–42. https://doi.org/10.1109/tnn.2009.2015974.\n\n\nChen, Chaofan, Oscar Li, Daniel Tao, Alina Barnett, Cynthia Rudin, and\nJonathan Su. 2019. “This Looks Like That: Deep\nLearning for Interpretable Image Recognition.” In Advances in\nNeural Information Processing Systems 32: Annual Conference on Neural\nInformation Processing Systems 2019, NeurIPS 2019, December 8-14, 2019,\nVancouver, BC, Canada, edited by Hanna M. Wallach, Hugo Larochelle,\nAlina Beygelzimer, Florence d’Alché-Buc, Emily B. Fox, and Roman\nGarnett, 8928–39. https://proceedings.neurips.cc/paper/2019/hash/adf7ee2dcf142b0e11888e72b43fcb75-Abstract.html.\n\n\nChen, Emma, Shvetank Prakash, Vijay Janapa Reddi, David Kim, and Pranav\nRajpurkar. 2023. “A Framework for Integrating Artificial\nIntelligence for Clinical Care with Continuous Therapeutic\nMonitoring.” Nat. Biomed. Eng., November. https://doi.org/10.1038/s41551-023-01115-0.\n\n\nChen, H.-W. 2006. “Gallium, Indium, and Arsenic Pollution of\nGroundwater from a Semiconductor Manufacturing Area of\nTaiwan.” B. Environ. Contam. Tox. 77 (2):\n289–96. https://doi.org/10.1007/s00128-006-1062-3.\n\n\nChen, Tianqi, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan,\nHaichen Shen, Meghan Cowan, et al. 2018. “TVM:\nAn Automated End-to-End Optimizing Compiler for Deep\nLearning.” In 13th USENIX Symposium on Operating Systems\nDesign and Implementation (OSDI 18), 578–94.\n\n\nChen, Tianqi, Bing Xu, Chiyuan Zhang, and Carlos Guestrin. 2016.\n“Training Deep Nets with Sublinear Memory Cost.” ArXiv\nPreprint abs/1604.06174. https://arxiv.org/abs/1604.06174.\n\n\nChen, Zhiyong, and Shugong Xu. 2023. “Learning\nDomain-Heterogeneous Speaker Recognition Systems with Personalized\nContinual Federated Learning.” EURASIP Journal on Audio,\nSpeech, and Music Processing 2023 (1): 33. https://doi.org/10.1186/s13636-023-00299-2.\n\n\nChen, Zitao, Guanpeng Li, Karthik Pattabiraman, and Nathan DeBardeleben.\n2019. “iBinFI/i: An Efficient Fault\nInjector for Safety-Critical Machine Learning Systems.” In\nProceedings of the International Conference for High Performance\nComputing, Networking, Storage and Analysis. SC ’19. New York, NY,\nUSA: ACM. https://doi.org/10.1145/3295500.3356177.\n\n\nChen, Zitao, Niranjhana Narayanan, Bo Fang, Guanpeng Li, Karthik\nPattabiraman, and Nathan DeBardeleben. 2020.\n“TensorFI: A Flexible Fault Injection\nFramework for TensorFlow Applications.” In 2020\nIEEE 31st International Symposium on Software Reliability Engineering\n(ISSRE), 426–35. IEEE; IEEE. https://doi.org/10.1109/issre5003.2020.00047.\n\n\nCheng, Eric, Shahrzad Mirkhani, Lukasz G. Szafaryn, Chen-Yong Cher,\nHyungmin Cho, Kevin Skadron, Mircea R. Stan, et al. 2016. “Clear:\nuC/u Ross u-l/u Ayer uE/u Xploration for uA/u Rchitecting uR/u Esilience\n- Combining Hardware and Software Techniques to Tolerate Soft Errors in\nProcessor Cores.” In Proceedings of the 53rd Annual Design\nAutomation Conference, 1–6. ACM. https://doi.org/10.1145/2897937.2897996.\n\n\nCheng, Yu, Duo Wang, Pan Zhou, and Tao Zhang. 2018. “Model\nCompression and Acceleration for Deep Neural Networks: The\nPrinciples, Progress, and Challenges.” IEEE Signal Process\nMag. 35 (1): 126–36. https://doi.org/10.1109/msp.2017.2765695.\n\n\nChi, Ping, Shuangchen Li, Cong Xu, Tao Zhang, Jishen Zhao, Yongpan Liu,\nYu Wang, and Yuan Xie. 2016. “Prime: A Novel Processing-in-Memory\nArchitecture for Neural Network Computation in ReRAM-Based Main\nMemory.” ACM SIGARCH Computer Architecture News 44 (3):\n27–39. https://doi.org/10.1145/3007787.3001140.\n\n\nChollet, François. 2018. “Introduction to Keras.” March\n9th.\n\n\nChristiano, Paul F., Jan Leike, Tom B. Brown, Miljan Martic, Shane Legg,\nand Dario Amodei. 2017. “Deep Reinforcement Learning from Human\nPreferences.” In Advances in Neural Information Processing\nSystems 30: Annual Conference on Neural Information Processing Systems\n2017, December 4-9, 2017, Long Beach, CA, USA, edited by Isabelle\nGuyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S.\nV. N. Vishwanathan, and Roman Garnett, 4299–4307. https://proceedings.neurips.cc/paper/2017/hash/d5e2c0adad503c91f91df240d0cd4e49-Abstract.html.\n\n\nChu, Grace, Okan Arikan, Gabriel Bender, Weijun Wang, Achille Brighton,\nPieter-Jan Kindermans, Hanxiao Liu, Berkin Akin, Suyog Gupta, and Andrew\nHoward. 2021. “Discovering Multi-Hardware Mobile Models via\nArchitecture Search.” In 2021 IEEE/CVF Conference on Computer\nVision and Pattern Recognition Workshops (CVPRW), 3022–31. IEEE. https://doi.org/10.1109/cvprw53098.2021.00337.\n\n\nChua, L. 1971. “Memristor-the Missing Circuit Element.”\n#IEEE_J_CT# 18 (5): 507–19. https://doi.org/10.1109/tct.1971.1083337.\n\n\nChung, Jae-Won, Yile Gu, Insu Jang, Luoxi Meng, Nikhil Bansal, and\nMosharaf Chowdhury. 2023. “Perseus: Removing Energy\nBloat from Large Model Training.” ArXiv Preprint\nabs/2312.06902. https://arxiv.org/abs/2312.06902.\n\n\nCohen, Maxime C., Ruben Lobel, and Georgia Perakis. 2016. “The\nImpact of Demand Uncertainty on Consumer Subsidies for Green Technology\nAdoption.” Manage. Sci. 62 (5): 1235–58. https://doi.org/10.1287/mnsc.2015.2173.\n\n\nColeman, Cody, Edward Chou, Julian Katz-Samuels, Sean Culatana, Peter\nBailis, Alexander C. Berg, Robert D. Nowak, Roshan Sumbaly, Matei\nZaharia, and I. Zeki Yalniz. 2022. “Similarity Search for\nEfficient Active Learning and Search of Rare Concepts.” In\nThirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022,\nThirty-Fourth Conference on Innovative Applications of Artificial\nIntelligence, IAAI 2022, the Twelveth Symposium on Educational Advances\nin Artificial Intelligence, EAAI 2022 Virtual Event, February 22 - March\n1, 2022, 6402–10. AAAI Press. https://ojs.aaai.org/index.php/AAAI/article/view/20591.\n\n\nColeman, Cody, Daniel Kang, Deepak Narayanan, Luigi Nardi, Tian Zhao,\nJian Zhang, Peter Bailis, Kunle Olukotun, Chris Ré, and Matei Zaharia.\n2019. “Analysis of DAWNBench, a Time-to-Accuracy\nMachine Learning Performance Benchmark.” ACM SIGOPS Operating\nSystems Review 53 (1): 14–25. https://doi.org/10.1145/3352020.3352024.\n\n\nConstantinescu, Cristian. 2008. “Intermittent Faults and Effects\non Reliability of Integrated Circuits.” In 2008 Annual\nReliability and Maintainability Symposium, 370–74. IEEE; IEEE. https://doi.org/10.1109/rams.2008.4925824.\n\n\nCooper, Tom, Suzanne Fallender, Joyann Pafumi, Jon Dettling, Sebastien\nHumbert, and Lindsay Lessard. 2011. “A Semiconductor Company’s\nExamination of Its Water Footprint Approach.” In Proceedings\nof the 2011 IEEE International Symposium on Sustainable Systems and\nTechnology, 1–6. IEEE; IEEE. https://doi.org/10.1109/issst.2011.5936865.\n\n\nCope, Gord. 2009. “Pure Water, Semiconductors and the\nRecession.” Global Water Intelligence 10 (10).\n\n\nCourbariaux, Matthieu, Itay Hubara, Daniel Soudry, Ran El-Yaniv, and\nYoshua Bengio. 2016. “Binarized Neural Networks:\nTraining Deep Neural Networks with Weights and Activations\nConstrained to+ 1 or-1.” arXiv Preprint\narXiv:1602.02830.\n\n\nD’ignazio, Catherine, and Lauren F Klein. 2023. Data Feminism.\nMIT press.\n\n\nDarvish Rouhani, Bita, Azalia Mirhoseini, and Farinaz Koushanfar. 2017.\n“TinyDL: Just-in-time\nDeep Learning Solution for Constrained Embedded Systems.” In\n2017 IEEE International Symposium on Circuits and Systems\n(ISCAS), 1–4. IEEE. https://doi.org/10.1109/iscas.2017.8050343.\n\n\nDavarzani, Samaneh, David Saucier, Purva Talegaonkar, Erin Parker, Alana\nTurner, Carver Middleton, Will Carroll, et al. 2023. “Closing the\nWearable Gap: Footankle\nKinematic Modeling via Deep Learning Models Based on a Smart Sock\nWearable.” Wearable Technologies 4. https://doi.org/10.1017/wtc.2023.3.\n\n\nDavid, Robert, Jared Duke, Advait Jain, Vijay Janapa Reddi, Nat\nJeffries, Jian Li, Nick Kreeger, et al. 2021. “Tensorflow Lite\nMicro: Embedded Machine Learning for Tinyml\nSystems.” Proceedings of Machine Learning and Systems 3:\n800–811.\n\n\nDavies, Emma. 2011. “Endangered Elements: Critical\nThinking.” https://www.rsc.org/images/Endangered\\%20Elements\\%20-\\%20Critical\\%20Thinking\\_tcm18-196054.pdf.\n\n\nDavies, Mike, Narayan Srinivasa, Tsung-Han Lin, Gautham Chinya,\nYongqiang Cao, Sri Harsha Choday, Georgios Dimou, et al. 2018.\n“Loihi: A Neuromorphic Manycore Processor with\non-Chip Learning.” IEEE Micro 38 (1): 82–99. https://doi.org/10.1109/mm.2018.112130359.\n\n\nDavies, Mike, Andreas Wild, Garrick Orchard, Yulia Sandamirskaya,\nGabriel A. Fonseca Guerra, Prasad Joshi, Philipp Plank, and Sumedh R.\nRisbud. 2021. “Advancing Neuromorphic Computing with Loihi:\nA Survey of Results and Outlook.” Proc.\nIEEE 109 (5): 911–34. https://doi.org/10.1109/jproc.2021.3067593.\n\n\nDavis, Jacqueline, Daniel Bizo, Andy Lawrence, Owen Rogers, and Max\nSmolaks. 2022. “Uptime Institute Global Data Center Survey\n2022.” Uptime Institute.\n\n\nDayarathna, Miyuru, Yonggang Wen, and Rui Fan. 2016. “Data Center\nEnergy Consumption Modeling: A Survey.” IEEE\nCommunications Surveys &Amp; Tutorials 18 (1): 732–94. https://doi.org/10.1109/comst.2015.2481183.\n\n\nDean, Jeffrey, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Quoc\nV. Le, Mark Z. Mao, et al. 2012. “Large Scale Distributed Deep\nNetworks.” In Advances in Neural Information Processing\nSystems 25: 26th Annual Conference on Neural Information Processing\nSystems 2012. Proceedings of a Meeting Held December 3-6, 2012, Lake\nTahoe, Nevada, United States, edited by Peter L. Bartlett, Fernando\nC. N. Pereira, Christopher J. C. Burges, Léon Bottou, and Kilian Q.\nWeinberger, 1232–40. https://proceedings.neurips.cc/paper/2012/hash/6aca97005c68f1206823815f66102863-Abstract.html.\n\n\nDeng, Jia, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Fei-Fei Li.\n2009. “ImageNet: A Large-Scale\nHierarchical Image Database.” In 2009 IEEE Conference on\nComputer Vision and Pattern Recognition, 248–55. IEEE. https://doi.org/10.1109/cvpr.2009.5206848.\n\n\nDesai, Tanvi, Felix Ritchie, Richard Welpton, et al. 2016. “Five\nSafes: Designing Data Access for Research.”\nEconomics Working Paper Series 1601: 28.\n\n\nDevlin, Jacob, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019.\n“BERT: Pre-training of\nDeep Bidirectional Transformers for Language Understanding.” In\nProceedings of the 2019 Conference of the North, 4171–86.\nMinneapolis, Minnesota: Association for Computational Linguistics. https://doi.org/10.18653/v1/n19-1423.\n\n\nDhar, Sauptik, Junyao Guo, Jiayi (Jason) Liu, Samarth Tripathi, Unmesh\nKurup, and Mohak Shah. 2021. “A Survey of on-Device Machine\nLearning: An Algorithms and Learning Theory Perspective.” ACM\nTransactions on Internet of Things 2 (3): 1–49. https://doi.org/10.1145/3450494.\n\n\nDong, Xin, Barbara De Salvo, Meng Li, Chiao Liu, Zhongnan Qu, H. T.\nKung, and Ziyun Li. 2022. “SplitNets:\nDesigning Neural Architectures for Efficient Distributed\nComputing on Head-Mounted Systems.” In 2022 IEEE/CVF\nConference on Computer Vision and Pattern Recognition (CVPR),\n12549–59. IEEE. https://doi.org/10.1109/cvpr52688.2022.01223.\n\n\nDongarra, Jack J. 2009. “The Evolution of High Performance\nComputing on System z.” IBM J. Res. Dev. 53: 3–4.\n\n\nDuarte, Javier, Nhan Tran, Ben Hawks, Christian Herwig, Jules Muhizi,\nShvetank Prakash, and Vijay Janapa Reddi. 2022.\n“FastML Science Benchmarks: Accelerating\nReal-Time Scientific Edge Machine Learning.” ArXiv\nPreprint abs/2207.07958. https://arxiv.org/abs/2207.07958.\n\n\nDuchi, John C., Elad Hazan, and Yoram Singer. 2010. “Adaptive\nSubgradient Methods for Online Learning and Stochastic\nOptimization.” In COLT 2010 - the 23rd Conference on Learning\nTheory, Haifa, Israel, June 27-29, 2010, edited by Adam Tauman\nKalai and Mehryar Mohri, 257–69. Omnipress. http://colt2010.haifa.il.ibm.com/papers/COLT2010proceedings.pdf#page=265.\n\n\nDuisterhof, Bardienus P, Srivatsan Krishnan, Jonathan J Cruz, Colby R\nBanbury, William Fu, Aleksandra Faust, Guido CHE de Croon, and Vijay\nJanapa Reddi. 2019. “Learning to Seek: Autonomous\nSource Seeking with Deep Reinforcement Learning Onboard a Nano Drone\nMicrocontroller.” ArXiv Preprint abs/1909.11236. https://arxiv.org/abs/1909.11236.\n\n\nDuisterhof, Bardienus P., Shushuai Li, Javier Burgues, Vijay Janapa\nReddi, and Guido C. H. E. de Croon. 2021. “Sniffy Bug:\nA Fully Autonomous Swarm of Gas-Seeking Nano Quadcopters in\nCluttered Environments.” In 2021 IEEE/RSJ International\nConference on Intelligent Robots and Systems (IROS), 9099–9106.\nIEEE; IEEE. https://doi.org/10.1109/iros51168.2021.9636217.\n\n\nDürr, Marc, Gunnar Nissen, Kurt-Wolfram Sühs, Philipp Schwenkenbecher,\nChristian Geis, Marius Ringelstein, Hans-Peter Hartung, et al. 2021.\n“CSF Findings in Acute NMDAR and LGI1 Antibody–Associated\nAutoimmune Encephalitis.” Neurology Neuroimmunology &Amp;\nNeuroinflammation 8 (6). https://doi.org/10.1212/nxi.0000000000001086.\n\n\nDwork, Cynthia, Frank McSherry, Kobbi Nissim, and Adam Smith. 2006.\n“Calibrating Noise to Sensitivity in Private Data\nAnalysis.” In Theory of Cryptography, edited by Shai\nHalevi and Tal Rabin, 265–84. Berlin, Heidelberg: Springer Berlin\nHeidelberg.\n\n\nDwork, Cynthia, and Aaron Roth. 2013. “The Algorithmic Foundations\nof Differential Privacy.” Foundations and Trends\nin Theoretical Computer Science 9 (3-4): 211–407. https://doi.org/10.1561/0400000042.\n\n\nEbrahimi, Khosrow, Gerard F. Jones, and Amy S. Fleischer. 2014. “A\nReview of Data Center Cooling Technology, Operating Conditions and the\nCorresponding Low-Grade Waste Heat Recovery Opportunities.”\nRenewable Sustainable Energy Rev. 31 (March): 622–38. https://doi.org/10.1016/j.rser.2013.12.007.\n\n\nEgwutuoha, Ifeanyi P., David Levy, Bran Selic, and Shiping Chen. 2013.\n“A Survey of Fault Tolerance Mechanisms and Checkpoint/Restart\nImplementations for High Performance Computing Systems.” The\nJournal of Supercomputing 65 (3): 1302–26. https://doi.org/10.1007/s11227-013-0884-0.\n\n\nEisenman, Assaf, Kiran Kumar Matam, Steven Ingram, Dheevatsa Mudigere,\nRaghuraman Krishnamoorthi, Krishnakumar Nair, Misha Smelyanskiy, and\nMurali Annavaram. 2022. “Check-n-Run: A Checkpointing\nSystem for Training Deep Learning Recommendation Models.” In\n19th USENIX Symposium on Networked Systems Design and Implementation\n(NSDI 22), 929–43.\n\n\nEldan, Ronen, and Mark Russinovich. 2023. “Who’s Harry Potter?\nApproximate Unlearning in LLMs.” ArXiv\nPreprint abs/2310.02238. https://arxiv.org/abs/2310.02238.\n\n\nEl-Rayis, A. O. 2014. “Reconfigurable Architectures for the Next\nGeneration of Mobile Device Telecommunications Systems.” :\nhttps://www.researchgate.net/publication/292608967.\n\n\nEshraghian, Jason K., Max Ward, Emre O. Neftci, Xinxin Wang, Gregor\nLenz, Girish Dwivedi, Mohammed Bennamoun, Doo Seok Jeong, and Wei D. Lu.\n2023. “Training Spiking Neural Networks Using Lessons from Deep\nLearning.” Proc. IEEE 111 (9): 1016–54. https://doi.org/10.1109/jproc.2023.3308088.\n\n\nEsteva, Andre, Brett Kuprel, Roberto A. Novoa, Justin Ko, Susan M.\nSwetter, Helen M. Blau, and Sebastian Thrun. 2017.\n“Dermatologist-Level Classification of Skin Cancer with Deep\nNeural Networks.” Nature 542 (7639): 115–18. https://doi.org/10.1038/nature21056.\n\n\nEykholt, Kevin, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati,\nChaowei Xiao, Atul Prakash, Tadayoshi Kohno, and Dawn Song. 2017.\n“Robust Physical-World Attacks on Deep Learning Models.”\nArXiv Preprint abs/1707.08945. https://arxiv.org/abs/1707.08945.\n\n\nFahim, Farah, Benjamin Hawks, Christian Herwig, James Hirschauer, Sergo\nJindariani, Nhan Tran, Luca P. Carloni, et al. 2021. “Hls4ml:\nAn Open-Source Codesign Workflow to Empower Scientific\nLow-Power Machine Learning Devices.” https://arxiv.org/abs/2103.05579.\n\n\nFarah, Martha J. 2005. “Neuroethics: The Practical\nand the Philosophical.” Trends Cogn. Sci. 9 (1): 34–40.\nhttps://doi.org/10.1016/j.tics.2004.12.001.\n\n\nFarwell, James P., and Rafal Rohozinski. 2011. “Stuxnet and the\nFuture of Cyber War.” Survival 53 (1): 23–40. https://doi.org/10.1080/00396338.2011.555586.\n\n\nFowers, Jeremy, Kalin Ovtcharov, Michael Papamichael, Todd Massengill,\nMing Liu, Daniel Lo, Shlomi Alkalay, et al. 2018. “A Configurable\nCloud-Scale DNN Processor for Real-Time\nAI.” In 2018 ACM/IEEE 45th Annual International\nSymposium on Computer Architecture (ISCA), 1–14. IEEE; IEEE. https://doi.org/10.1109/isca.2018.00012.\n\n\nFrancalanza, Adrian, Luca Aceto, Antonis Achilleos, Duncan Paul Attard,\nIan Cassar, Dario Della Monica, and Anna Ingólfsdóttir. 2017. “A\nFoundation for Runtime Monitoring.” In International\nConference on Runtime Verification, 8–29. Springer.\n\n\nFrankle, Jonathan, and Michael Carbin. 2019. “The Lottery Ticket\nHypothesis: Finding Sparse, Trainable Neural\nNetworks.” In 7th International Conference on Learning\nRepresentations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019.\nOpenReview.net. https://openreview.net/forum?id=rJl-b3RcF7.\n\n\nFriedman, Batya. 1996. “Value-Sensitive Design.”\nInteractions 3 (6): 16–23. https://doi.org/10.1145/242485.242493.\n\n\nFurber, Steve. 2016. “Large-Scale Neuromorphic Computing\nSystems.” J. Neural Eng. 13 (5): 051001. https://doi.org/10.1088/1741-2560/13/5/051001.\n\n\nFursov, Ivan, Matvey Morozov, Nina Kaploukhaya, Elizaveta Kovtun,\nRodrigo Rivera-Castro, Gleb Gusev, Dmitry Babaev, Ivan Kireev, Alexey\nZaytsev, and Evgeny Burnaev. 2021. “Adversarial Attacks on Deep\nModels for Financial Transaction Records.” In Proceedings of\nthe 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data\nMining, 2868–78. ACM. https://doi.org/10.1145/3447548.3467145.\n\n\nGale, Trevor, Erich Elsen, and Sara Hooker. 2019. “The State of\nSparsity in Deep Neural Networks.” ArXiv Preprint\nabs/1902.09574. https://arxiv.org/abs/1902.09574.\n\n\nGandolfi, Karine, Christophe Mourtel, and Francis Olivier. 2001.\n“Electromagnetic Analysis: Concrete Results.”\nIn Cryptographic Hardware and Embedded SystemsCHES\n2001: Third International Workshop Paris, France, May 1416,\n2001 Proceedings 3, 251–61. Springer.\n\n\nGannot, G., and M. Ligthart. 1994. “Verilog HDL Based\nFPGA Design.” In International Verilog HDL\nConference, 86–92. IEEE. https://doi.org/10.1109/ivc.1994.323743.\n\n\nGao, Yansong, Said F. Al-Sarawi, and Derek Abbott. 2020. “Physical\nUnclonable Functions.” Nature Electronics 3 (2): 81–91.\nhttps://doi.org/10.1038/s41928-020-0372-5.\n\n\nGates, Byron D. 2009. “Flexible Electronics.”\nScience 323 (5921): 1566–67. https://doi.org/10.1126/science.1171230.\n\n\nGebru, Timnit, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman\nVaughan, Hanna Wallach, Hal Daumé III, and Kate Crawford. 2021.\n“Datasheets for Datasets.” Commun. ACM 64 (12):\n86–92. https://doi.org/10.1145/3458723.\n\n\nGeiger, Atticus, Hanson Lu, Thomas Icard, and Christopher Potts. 2021.\n“Causal Abstractions of Neural Networks.” In Advances\nin Neural Information Processing Systems 34: Annual Conference on Neural\nInformation Processing Systems 2021, NeurIPS 2021, December 6-14, 2021,\nVirtual, edited by Marc’Aurelio Ranzato, Alina Beygelzimer, Yann N.\nDauphin, Percy Liang, and Jennifer Wortman Vaughan, 9574–86. https://proceedings.neurips.cc/paper/2021/hash/4f5c422f4d49a5a807eda27434231040-Abstract.html.\n\n\nGholami, Dong Kim, Mahoney Yao, and Keutzer. 2021. “A Survey of\nQuantization Methods for Efficient Neural Network Inference).”\nArXiv Preprint. https://arxiv.org/abs/2103.13630.\n\n\nGlorot, Xavier, and Yoshua Bengio. 2010. “Understanding the\nDifficulty of Training Deep Feedforward Neural Networks.” In\nProceedings of the Thirteenth International Conference on Artificial\nIntelligence and Statistics, 249–56. http://proceedings.mlr.press/v9/glorot10a.html.\n\n\nGnad, Dennis R. E., Fabian Oboril, and Mehdi B. Tahoori. 2017.\n“Voltage Drop-Based Fault Attacks on FPGAs Using\nValid Bitstreams.” In 2017 27th International Conference on\nField Programmable Logic and Applications (FPL), 1–7. IEEE; IEEE.\nhttps://doi.org/10.23919/fpl.2017.8056840.\n\n\nGoodfellow, Ian, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David\nWarde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2020.\n“Generative Adversarial Networks.” Commun. ACM 63\n(11): 139–44. https://doi.org/10.1145/3422622.\n\n\nGoodyear, Victoria A. 2017. “Social Media, Apps and Wearable\nTechnologies: Navigating Ethical Dilemmas and\nProcedures.” Qualitative Research in Sport, Exercise and\nHealth 9 (3): 285–302. https://doi.org/10.1080/2159676x.2017.1303790.\n\n\nGoogle. n.d. “Information Quality Content Moderation.” https://blog.google/documents/83/.\n\n\nGordon, Ariel, Elad Eban, Ofir Nachum, Bo Chen, Hao Wu, Tien-Ju Yang,\nand Edward Choi. 2018. “MorphNet: Fast\n&Amp; Simple Resource-Constrained Structure Learning of Deep\nNetworks.” In 2018 IEEE/CVF Conference on Computer Vision and\nPattern Recognition, 1586–95. IEEE. https://doi.org/10.1109/cvpr.2018.00171.\n\n\nGräfe, Ralf, Qutub Syed Sha, Florian Geissler, and Michael Paulitsch.\n2023. “Large-Scale Application of Fault Injection into\nPyTorch Models -an Extension to PyTorchFI for\nValidation Efficiency.” In 2023 53rd Annual IEEE/IFIP\nInternational Conference on Dependable Systems and Networks -\nSupplemental Volume (DSN-s), 56–62. IEEE; IEEE. https://doi.org/10.1109/dsn-s58398.2023.00025.\n\n\nGreengard, Samuel. 2015. The Internet of Things. The MIT Press.\nhttps://doi.org/10.7551/mitpress/10277.001.0001.\n\n\nGrossman, Elizabeth. 2007. High Tech Trash: Digital\nDevices, Hidden Toxics, and Human Health. Island press.\n\n\nGruslys, Audrunas, Rémi Munos, Ivo Danihelka, Marc Lanctot, and Alex\nGraves. 2016. “Memory-Efficient Backpropagation Through\nTime.” In Advances in Neural Information Processing Systems\n29: Annual Conference on Neural Information Processing Systems 2016,\nDecember 5-10, 2016, Barcelona, Spain, edited by Daniel D. Lee,\nMasashi Sugiyama, Ulrike von Luxburg, Isabelle Guyon, and Roman Garnett,\n4125–33. https://proceedings.neurips.cc/paper/2016/hash/a501bebf79d570651ff601788ea9d16d-Abstract.html.\n\n\nGu, Ivy. 2023. “Deep Learning Model Compression (Ii) by Ivy Gu\nMedium.” https://ivygdy.medium.com/deep-learning-model-compression-ii-546352ea9453.\n\n\nGuo, Chuan, Jacob Gardner, Yurong You, Andrew Gordon Wilson, and Kilian\nWeinberger. 2019. “Simple Black-Box Adversarial Attacks.”\nIn International Conference on Machine Learning, 2484–93. PMLR.\n\n\nGuo, Yutao, Hao Wang, Hui Zhang, Tong Liu, Zhaoguang Liang, Yunlong Xia,\nLi Yan, et al. 2019. “Mobile Photoplethysmographic Technology to\nDetect Atrial Fibrillation.” J. Am. Coll. Cardiol. 74\n(19): 2365–75. https://doi.org/10.1016/j.jacc.2019.08.019.\n\n\nGupta, Maanak, Charankumar Akiri, Kshitiz Aryal, Eli Parker, and\nLopamudra Praharaj. 2023. “From ChatGPT to\nThreatGPT: Impact of Generative\nAI in Cybersecurity and Privacy.”\n#IEEE_O_ACC# 11: 80218–45. https://doi.org/10.1109/access.2023.3300381.\n\n\nGupta, Maya, Andrew Cotter, Jan Pfeifer, Konstantin Voevodski, Kevin\nCanini, Alexander Mangylov, Wojciech Moczydlowski, and Alexander Van\nEsbroeck. 2016. “Monotonic Calibrated Interpolated Look-up\nTables.” The Journal of Machine Learning Research 17\n(1): 3790–3836.\n\n\nGupta, Udit, Mariam Elgamal, Gage Hills, Gu-Yeon Wei, Hsien-Hsin S. Lee,\nDavid Brooks, and Carole-Jean Wu. 2022. “Act: Designing\nSustainable Computer Systems with an Architectural Carbon Modeling\nTool.” In Proceedings of the 49th Annual International\nSymposium on Computer Architecture, 784–99. ACM. https://doi.org/10.1145/3470496.3527408.\n\n\nGwennap, Linley. n.d. “Certus-NX Innovates\nGeneral-Purpose FPGAs.”\n\n\nHaensch, Wilfried, Tayfun Gokmen, and Ruchir Puri. 2019. “The Next\nGeneration of Deep Learning Hardware: Analog\nComputing.” Proc. IEEE 107 (1): 108–22. https://doi.org/10.1109/jproc.2018.2871057.\n\n\nHamming, R. W. 1950. “Error Detecting and Error Correcting\nCodes.” Bell Syst. Tech. J. 29 (2): 147–60. https://doi.org/10.1002/j.1538-7305.1950.tb00463.x.\n\n\nHan, Song, Huizi Mao, and William J Dally. 2015. “Deep\nCompression: Compressing Deep Neural Networks with Pruning,\nTrained Quantization and Huffman Coding.” arXiv Preprint\narXiv:1510.00149.\n\n\nHan, Song, Huizi Mao, and William J. Dally. 2016. “Deep\nCompression: Compressing Deep Neural Networks with Pruning,\nTrained Quantization and Huffman Coding.” https://arxiv.org/abs/1510.00149.\n\n\nHandlin, Oscar. 1965. “Science and Technology in Popular\nCulture.” Daedalus-Us., 156–70.\n\n\nHardt, Moritz, Eric Price, and Nati Srebro. 2016. “Equality of\nOpportunity in Supervised Learning.” In Advances in Neural\nInformation Processing Systems 29: Annual Conference on Neural\nInformation Processing Systems 2016, December 5-10, 2016, Barcelona,\nSpain, edited by Daniel D. Lee, Masashi Sugiyama, Ulrike von\nLuxburg, Isabelle Guyon, and Roman Garnett, 3315–23. https://proceedings.neurips.cc/paper/2016/hash/9d2682367c3935defcb1f9e247a97c0d-Abstract.html.\n\n\nHawks, Benjamin, Javier Duarte, Nicholas J. Fraser, Alessandro\nPappalardo, Nhan Tran, and Yaman Umuroglu. 2021. “Ps and Qs: Quantization-aware Pruning for Efficient Low\nLatency Neural Network Inference.” Frontiers in Artificial\nIntelligence 4 (July). https://doi.org/10.3389/frai.2021.676564.\n\n\nHazan, Avi, and Elishai Ezra Tsur. 2021. “Neuromorphic Analog\nImplementation of Neural Engineering Framework-Inspired Spiking Neuron\nfor High-Dimensional Representation.” Front. Neurosci.\n15 (February): 627221. https://doi.org/10.3389/fnins.2021.627221.\n\n\nHe, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2015.\n“Delving Deep into Rectifiers: Surpassing Human-Level Performance\non ImageNet Classification.” In 2015 IEEE International\nConference on Computer Vision (ICCV), 1026–34. IEEE. https://doi.org/10.1109/iccv.2015.123.\n\n\n———. 2016. “Deep Residual Learning for Image Recognition.”\nIn 2016 IEEE Conference on Computer Vision and Pattern Recognition\n(CVPR), 770–78. IEEE. https://doi.org/10.1109/cvpr.2016.90.\n\n\nHe, Yi, Prasanna Balaprakash, and Yanjing Li. 2020.\n“FIdelity: Efficient Resilience Analysis\nFramework for Deep Learning Accelerators.” In 2020 53rd\nAnnual IEEE/ACM International Symposium on Microarchitecture\n(MICRO), 270–81. IEEE; IEEE. https://doi.org/10.1109/micro50266.2020.00033.\n\n\nHe, Yi, Mike Hutton, Steven Chan, Robert De Gruijl, Rama Govindaraju,\nNishant Patil, and Yanjing Li. 2023. “Understanding and Mitigating\nHardware Failures in Deep Learning Training Systems.” In\nProceedings of the 50th Annual International Symposium on Computer\nArchitecture, 1–16. IEEE; ACM. https://doi.org/10.1145/3579371.3589105.\n\n\nHébert-Johnson, Úrsula, Michael P. Kim, Omer Reingold, and Guy N.\nRothblum. 2018. “Multicalibration: Calibration for\nthe (Computationally-Identifiable) Masses.” In\nProceedings of the 35th International Conference on Machine\nLearning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15,\n2018, edited by Jennifer G. Dy and Andreas Krause, 80:1944–53.\nProceedings of Machine Learning Research. PMLR. http://proceedings.mlr.press/v80/hebert-johnson18a.html.\n\n\nHegde, Sumant. 2023. “An Introduction to Separable Convolutions -\nAnalytics Vidhya.” https://www.analyticsvidhya.com/blog/2021/11/an-introduction-to-separable-convolutions/.\n\n\nHenderson, Peter, Jieru Hu, Joshua Romoff, Emma Brunskill, Dan Jurafsky,\nand Joelle Pineau. 2020. “Towards the Systematic Reporting of the\nEnergy and Carbon Footprints of Machine Learning.” The\nJournal of Machine Learning Research 21 (1): 10039–81.\n\n\nHendrycks, Dan, and Thomas Dietterich. 2019. “Benchmarking Neural\nNetwork Robustness to Common Corruptions and Perturbations.”\narXiv Preprint arXiv:1903.12261.\n\n\nHendrycks, Dan, Kevin Zhao, Steven Basart, Jacob Steinhardt, and Dawn\nSong. 2021. “Natural Adversarial Examples.” In 2021\nIEEE/CVF Conference on Computer Vision and Pattern Recognition\n(CVPR), 15262–71. IEEE. https://doi.org/10.1109/cvpr46437.2021.01501.\n\n\nHennessy, John L., and David A. Patterson. 2019. “A New Golden Age\nfor Computer Architecture.” Commun. ACM 62 (2): 48–60.\nhttps://doi.org/10.1145/3282307.\n\n\nHimmelstein, Gracie, David Bates, and Li Zhou. 2022. “Examination\nof Stigmatizing Language in the Electronic Health Record.”\nJAMA Network Open 5 (1): e2144967. https://doi.org/10.1001/jamanetworkopen.2021.44967.\n\n\nHinton, Geoffrey. 2005. “Van Nostrand’s Scientific Encyclopedia.” Wiley.\nhttps://doi.org/10.1002/0471743984.vse0673.\n\n\n———. 2017. “Overview of Minibatch Gradient Descent.”\nUniversity of Toronto; University Lecture.\n\n\nHo Yoon, Jung, Hyung-Suk Jung, Min Hwan Lee, Gun Hwan Kim, Seul Ji Song,\nJun Yeong Seok, Kyung Jean Yoon, et al. 2012. “Frontiers in\nElectronic Materials.” Wiley. https://doi.org/10.1002/9783527667703.ch67.\n\n\nHoefler, Torsten, Dan Alistarh, Tal Ben-Nun, Nikoli Dryden, and\nAlexandra Peste. 2021. “Sparsity in Deep Learning: Pruning and\nGrowth for Efficient Inference and Training in Neural Networks,”\nJanuary. http://arxiv.org/abs/2102.00554v1.\n\n\nHolland, Sarah, Ahmed Hosny, Sarah Newman, Joshua Joseph, and Kasia\nChmielinski. 2020. “The Dataset Nutrition Label: A Framework to\nDrive Higher Data Quality Standards.” In Data Protection and\nPrivacy. Hart Publishing. https://doi.org/10.5040/9781509932771.ch-001.\n\n\nHong, Sanghyun, Nicholas Carlini, and Alexey Kurakin. 2023.\n“Publishing Efficient on-Device Models Increases Adversarial\nVulnerability.” In 2023 IEEE Conference on Secure and\nTrustworthy Machine Learning (SaTML), 271–90. IEEE; IEEE. https://doi.org/10.1109/satml54575.2023.00026.\n\n\nHosseini, Hossein, Sreeram Kannan, Baosen Zhang, and Radha Poovendran.\n2017. “Deceiving Google’s Perspective Api Built for Detecting\nToxic Comments.” ArXiv Preprint abs/1702.08138. https://arxiv.org/abs/1702.08138.\n\n\nHoward, Andrew G., Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun\nWang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. 2017.\n“MobileNets: Efficient Convolutional\nNeural Networks for Mobile Vision Applications.” ArXiv\nPreprint. https://arxiv.org/abs/1704.04861.\n\n\nHsiao, Yu-Shun, Zishen Wan, Tianyu Jia, Radhika Ghosal, Abdulrahman\nMahmoud, Arijit Raychowdhury, David Brooks, Gu-Yeon Wei, and Vijay\nJanapa Reddi. 2023. “MAVFI: An\nEnd-to-End Fault Analysis Framework with Anomaly Detection and Recovery\nfor Micro Aerial Vehicles.” In 2023 Design, Automation\n&Amp; Test in Europe Conference &Amp; Exhibition (DATE),\n1–6. IEEE; IEEE. https://doi.org/10.23919/date56975.2023.10137246.\n\n\nHsu, Liang-Ching, Ching-Yi Huang, Yen-Hsun Chuang, Ho-Wen Chen, Ya-Ting\nChan, Heng Yi Teah, Tsan-Yao Chen, Chiung-Fen Chang, Yu-Ting Liu, and\nYu-Min Tzou. 2016. “Accumulation of Heavy Metals and Trace\nElements in Fluvial Sediments Received Effluents from Traditional and\nSemiconductor Industries.” Scientific Reports 6 (1):\n34250. https://doi.org/10.1038/srep34250.\n\n\nHu, Jie, Li Shen, and Gang Sun. 2018. “Squeeze-and-Excitation\nNetworks.” In 2018 IEEE/CVF Conference on Computer Vision and\nPattern Recognition, 7132–41. IEEE. https://doi.org/10.1109/cvpr.2018.00745.\n\n\nHu, Yang, Jie Jiang, Lifu Zhang, Yunfeng Shi, and Jian Shi. 2023.\n“Halide Perovskite Semiconductors.” Wiley. https://doi.org/10.1002/9783527829026.ch13.\n\n\nHuang, Tsung-Ching, Kenjiro Fukuda, Chun-Ming Lo, Yung-Hui Yeh, Tsuyoshi\nSekitani, Takao Someya, and Kwang-Ting Cheng. 2011.\n“Pseudo-CMOS: A Design Style for\nLow-Cost and Robust Flexible Electronics.” IEEE Trans.\nElectron Devices 58 (1): 141–50. https://doi.org/10.1109/ted.2010.2088127.\n\n\nHutter, Michael, Jorn-Marc Schmidt, and Thomas Plos. 2009.\n“Contact-Based Fault Injections and Power Analysis on\nRFID Tags.” In 2009 European Conference on\nCircuit Theory and Design, 409–12. IEEE; IEEE. https://doi.org/10.1109/ecctd.2009.5275012.\n\n\nIandola, Forrest N, Song Han, Matthew W Moskewicz, Khalid Ashraf,\nWilliam J Dally, and Kurt Keutzer. 2016. “SqueezeNet:\nAlexnet-level Accuracy with 50x Fewer\nParameters and 0.5 MB Model Size.” ArXiv\nPreprint abs/1602.07360. https://arxiv.org/abs/1602.07360.\n\n\nIgnatov, Andrey, Radu Timofte, William Chou, Ke Wang, Max Wu, Tim\nHartley, and Luc Van Gool. 2018. “AI Benchmark:\nRunning Deep Neural Networks on Android\nSmartphones,” 0–0.\n\n\nImani, Mohsen, Abbas Rahimi, and Tajana S. Rosing. 2016.\n“Resistive Configurable Associative Memory for Approximate\nComputing.” In Proceedings of the 2016 Design, Automation\n&Amp; Test in Europe Conference &Amp; Exhibition (DATE),\n1327–32. IEEE; Research Publishing Services. https://doi.org/10.3850/9783981537079_0454.\n\n\nIntelLabs. 2023. “Knowledge Distillation - Neural Network\nDistiller.” https://intellabs.github.io/distiller/knowledge_distillation.html.\n\n\nIppolito, Daphne, Florian Tramer, Milad Nasr, Chiyuan Zhang, Matthew\nJagielski, Katherine Lee, Christopher Choquette Choo, and Nicholas\nCarlini. 2023. “Preventing Generation of Verbatim Memorization in\nLanguage Models Gives a False Sense of Privacy.” In\nProceedings of the 16th International Natural Language Generation\nConference, 5253–70. Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.inlg-main.3.\n\n\nIrimia-Vladu, Mihai. 2014.\n““Green” Electronics:\nBiodegradable and Biocompatible Materials and Devices for\nSustainable Future.” Chem. Soc. Rev. 43 (2): 588–610. https://doi.org/10.1039/c3cs60235d.\n\n\nIsscc. 2014. “Computing’s Energy Problem (and What We Can Do about\nIt).” https://ieeexplore.ieee.org/document/6757323.\n\n\nJacob, Benoit, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang,\nAndrew Howard, Hartwig Adam, and Dmitry Kalenichenko. 2018.\n“Quantization and Training of Neural Networks for Efficient\nInteger-Arithmetic-Only Inference.” In Proceedings of the\nIEEE Conference on Computer Vision and Pattern Recognition,\n2704–13.\n\n\nJaderberg, Max, Valentin Dalibard, Simon Osindero, Wojciech M.\nCzarnecki, Jeff Donahue, Ali Razavi, Oriol Vinyals, et al. 2017.\n“Population Based Training of Neural Networks.” arXiv\nPreprint arXiv:1711.09846, November. http://arxiv.org/abs/1711.09846v2.\n\n\nJanapa Reddi, Vijay, Alexander Elium, Shawn Hymel, David Tischler,\nDaniel Situnayake, Carl Ward, Louis Moreau, et al. 2023. “Edge\nImpulse: An MLOps Platform for Tiny Machine\nLearning.” Proceedings of Machine Learning and Systems\n5.\n\n\nJha, A. R. 2014. Rare Earth Materials: Properties and\nApplications. CRC Press. https://doi.org/10.1201/b17045.\n\n\nJha, Saurabh, Subho Banerjee, Timothy Tsai, Siva K. S. Hari, Michael B.\nSullivan, Zbigniew T. Kalbarczyk, Stephen W. Keckler, and Ravishankar K.\nIyer. 2019. “ML-Based Fault Injection for Autonomous\nVehicles: A Case for Bayesian Fault\nInjection.” In 2019 49th Annual IEEE/IFIP International\nConference on Dependable Systems and Networks (DSN), 112–24. IEEE;\nIEEE. https://doi.org/10.1109/dsn.2019.00025.\n\n\nJia, Yangqing, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan\nLong, Ross Girshick, Sergio Guadarrama, and Trevor Darrell. 2014.\n“Caffe: Convolutional Architecture for Fast Feature\nEmbedding.” In Proceedings of the 22nd ACM International\nConference on Multimedia, 675–78. ACM. https://doi.org/10.1145/2647868.2654889.\n\n\nJia, Zhe, Marco Maggioni, Benjamin Staiger, and Daniele P. Scarpazza.\n2018. “Dissecting the NVIDIA Volta\nGPU Architecture via Microbenchmarking.” ArXiv\nPreprint. https://arxiv.org/abs/1804.06826.\n\n\nJia, Zhenge, Dawei Li, Xiaowei Xu, Na Li, Feng Hong, Lichuan Ping, and\nYiyu Shi. 2023. “Life-Threatening Ventricular Arrhythmia Detection\nChallenge in Implantable\nCardioverterdefibrillators.” Nature Machine\nIntelligence 5 (5): 554–55. https://doi.org/10.1038/s42256-023-00659-9.\n\n\nJia, Zhihao, Matei Zaharia, and Alex Aiken. 2019. “Beyond Data and\nModel Parallelism for Deep Neural Networks.” In Proceedings\nof Machine Learning and Systems 2019, MLSys 2019, Stanford, CA, USA,\nMarch 31 - April 2, 2019, edited by Ameet Talwalkar, Virginia\nSmith, and Matei Zaharia. mlsys.org. https://proceedings.mlsys.org/book/265.pdf.\n\n\nJin, Yilun, Xiguang Wei, Yang Liu, and Qiang Yang. 2020. “Towards\nUtilizing Unlabeled Data in Federated Learning: A Survey\nand Prospective.” arXiv Preprint arXiv:2002.11545.\n\n\nJohnson-Roberson, Matthew, Charles Barto, Rounak Mehta, Sharath Nittur\nSridhar, Karl Rosaen, and Ram Vasudevan. 2017. “Driving in the\nMatrix: Can Virtual Worlds Replace Human-Generated\nAnnotations for Real World Tasks?” In 2017 IEEE International\nConference on Robotics and Automation (ICRA), 746–53. Singapore,\nSingapore: IEEE. https://doi.org/10.1109/icra.2017.7989092.\n\n\nJouppi, Norman P., Cliff Young, Nishant Patil, David Patterson, Gaurav\nAgrawal, Raminder Bajwa, Sarah Bates, et al. 2017a. “In-Datacenter\nPerformance Analysis of a Tensor Processing Unit.” In\nProceedings of the 44th Annual International Symposium on Computer\nArchitecture, 1–12. ISCA ’17. New York, NY, USA: ACM. https://doi.org/10.1145/3079856.3080246.\n\n\n———, et al. 2017b. “In-Datacenter Performance Analysis of a Tensor\nProcessing Unit.” In Proceedings of the 44th Annual\nInternational Symposium on Computer Architecture, 1–12. ISCA ’17.\nNew York, NY, USA: ACM. https://doi.org/10.1145/3079856.3080246.\n\n\nJouppi, Norm, George Kurian, Sheng Li, Peter Ma, Rahul Nagarajan, Lifeng\nNai, Nishant Patil, et al. 2023. “TPU V4:\nAn Optically Reconfigurable Supercomputer for Machine\nLearning with Hardware Support for Embeddings.” In\nProceedings of the 50th Annual International Symposium on Computer\nArchitecture. ISCA ’23. New York, NY, USA: ACM. https://doi.org/10.1145/3579371.3589350.\n\n\nJoye, Marc, and Michael Tunstall. 2012. Fault Analysis in\nCryptography. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-29656-7.\n\n\nKairouz, Peter, Sewoong Oh, and Pramod Viswanath. 2015. “Secure\nMulti-Party Differential Privacy.” In Advances in Neural\nInformation Processing Systems 28: Annual Conference on Neural\nInformation Processing Systems 2015, December 7-12, 2015, Montreal,\nQuebec, Canada, edited by Corinna Cortes, Neil D. Lawrence, Daniel\nD. Lee, Masashi Sugiyama, and Roman Garnett, 2008–16. https://proceedings.neurips.cc/paper/2015/hash/a01610228fe998f515a72dd730294d87-Abstract.html.\n\n\nKalamkar, Dhiraj, Dheevatsa Mudigere, Naveen Mellempudi, Dipankar Das,\nKunal Banerjee, Sasikanth Avancha, Dharma Teja Vooturi, et al. 2019.\n“A Study of BFLOAT16 for Deep Learning\nTraining.” https://arxiv.org/abs/1905.12322.\n\n\nKao, Sheng-Chun, Geonhwa Jeong, and Tushar Krishna. 2020.\n“ConfuciuX: Autonomous Hardware Resource\nAssignment for DNN Accelerators Using Reinforcement\nLearning.” In 2020 53rd Annual IEEE/ACM International\nSymposium on Microarchitecture (MICRO), 622–36. IEEE; IEEE. https://doi.org/10.1109/micro50266.2020.00058.\n\n\nKao, Sheng-Chun, and Tushar Krishna. 2020. “Gamma: Automating the\nHW Mapping of DNN Models on Accelerators via Genetic Algorithm.”\nIn Proceedings of the 39th International Conference on\nComputer-Aided Design, 1–9. ACM. https://doi.org/10.1145/3400302.3415639.\n\n\nKaplan, Jared, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin\nChess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario\nAmodei. 2020. “Scaling Laws for Neural Language Models.”\nArXiv Preprint abs/2001.08361. https://arxiv.org/abs/2001.08361.\n\n\nKarargyris, Alexandros, Renato Umeton, Micah J Sheller, Alejandro\nAristizabal, Johnu George, Anna Wuest, Sarthak Pati, et al. 2023.\n“Federated Benchmarking of Medical Artificial Intelligence with\nMedPerf.” Nature Machine Intelligence 5\n(7): 799–810. https://doi.org/10.1038/s42256-023-00652-2.\n\n\nKaur, Harmanpreet, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna\nWallach, and Jennifer Wortman Vaughan. 2020. “Interpreting\nInterpretability: Understanding Data Scientists’ Use of\nInterpretability Tools for Machine Learning.” In Proceedings\nof the 2020 CHI Conference on Human Factors in Computing Systems,\nedited by Regina Bernhaupt, Florian ’Floyd’Mueller, David Verweij, Josh\nAndres, Joanna McGrenere, Andy Cockburn, Ignacio Avellino, et al., 1–14.\nACM. https://doi.org/10.1145/3313831.3376219.\n\n\nKawazoe Aguilera, Marcos, Wei Chen, and Sam Toueg. 1997.\n“Heartbeat: A Timeout-Free Failure Detector for\nQuiescent Reliable Communication.” In Distributed Algorithms:\n11th International Workshop, WDAG’97 Saarbrücken, Germany, September\n2426, 1997 Proceedings 11, 126–40. Springer.\n\n\nKhan, Mohammad Emtiyaz, and Siddharth Swaroop. 2021.\n“Knowledge-Adaptation Priors.” In Advances in Neural\nInformation Processing Systems 34: Annual Conference on Neural\nInformation Processing Systems 2021, NeurIPS 2021, December 6-14, 2021,\nVirtual, edited by Marc’Aurelio Ranzato, Alina Beygelzimer, Yann N.\nDauphin, Percy Liang, and Jennifer Wortman Vaughan, 19757–70. https://proceedings.neurips.cc/paper/2021/hash/a4380923dd651c195b1631af7c829187-Abstract.html.\n\n\nKiela, Douwe, Max Bartolo, Yixin Nie, Divyansh Kaushik, Atticus Geiger,\nZhengxuan Wu, Bertie Vidgen, et al. 2021. “Dynabench:\nRethinking Benchmarking in NLP.” In\nProceedings of the 2021 Conference of the North American Chapter of\nthe Association for Computational Linguistics: Human Language\nTechnologies, 4110–24. Online: Association for Computational\nLinguistics. https://doi.org/10.18653/v1/2021.naacl-main.324.\n\n\nKim, Jungrae, Michael Sullivan, and Mattan Erez. 2015. “Bamboo\nECC: Strong, Safe, and Flexible Codes for\nReliable Computer Memory.” In 2015 IEEE 21st International\nSymposium on High Performance Computer Architecture (HPCA), 101–12.\nIEEE; IEEE. https://doi.org/10.1109/hpca.2015.7056025.\n\n\nKim, Sunju, Chungsik Yoon, Seunghon Ham, Jihoon Park, Ohun Kwon, Donguk\nPark, Sangjun Choi, Seungwon Kim, Kwonchul Ha, and Won Kim. 2018.\n“Chemical Use in the Semiconductor Manufacturing Industry.”\nInt. J. Occup. Env. Heal. 24 (3-4): 109–18. https://doi.org/10.1080/10773525.2018.1519957.\n\n\nKingma, Diederik P., and Jimmy Ba. 2014. “Adam: A Method for\nStochastic Optimization.” Edited by Yoshua Bengio and Yann LeCun,\nDecember. http://arxiv.org/abs/1412.6980v9.\n\n\nKirkpatrick, James, Razvan Pascanu, Neil Rabinowitz, Joel Veness,\nGuillaume Desjardins, Andrei A. Rusu, Kieran Milan, et al. 2017.\n“Overcoming Catastrophic Forgetting in Neural Networks.”\nProc. Natl. Acad. Sci. 114 (13): 3521–26. https://doi.org/10.1073/pnas.1611835114.\n\n\nKo, Yohan. 2021. “Characterizing System-Level Masking Effects\nAgainst Soft Errors.” Electronics 10 (18): 2286. https://doi.org/10.3390/electronics10182286.\n\n\nKocher, Paul, Jann Horn, Anders Fogh, Daniel Genkin, Daniel Gruss,\nWerner Haas, Mike Hamburg, et al. 2019a. “Spectre Attacks:\nExploiting Speculative Execution.” In 2019 IEEE\nSymposium on Security and Privacy (SP). IEEE. https://doi.org/10.1109/sp.2019.00002.\n\n\n———, et al. 2019b. “Spectre Attacks: Exploiting\nSpeculative Execution.” In 2019 IEEE Symposium on Security\nand Privacy (SP). IEEE. https://doi.org/10.1109/sp.2019.00002.\n\n\nKocher, Paul, Joshua Jaffe, and Benjamin Jun. 1999. “Differential\nPower Analysis.” In Advances in\nCryptologyCRYPTO’99: 19th Annual International Cryptology\nConference Santa Barbara, California, USA, August 1519,\n1999 Proceedings 19, 388–97. Springer.\n\n\nKocher, Paul, Joshua Jaffe, Benjamin Jun, and Pankaj Rohatgi. 2011.\n“Introduction to Differential Power Analysis.” Journal\nof Cryptographic Engineering 1 (1): 5–27. https://doi.org/10.1007/s13389-011-0006-y.\n\n\nKoh, Pang Wei, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma\nPierson, Been Kim, and Percy Liang. 2020. “Concept Bottleneck\nModels.” In Proceedings of the 37th International Conference\non Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event,\n119:5338–48. Proceedings of Machine Learning Research. PMLR. http://proceedings.mlr.press/v119/koh20a.html.\n\n\nKoh, Pang Wei, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin\nZhang, Akshay Balsubramani, Weihua Hu, et al. 2021.\n“WILDS: A Benchmark of in-the-Wild\nDistribution Shifts.” In Proceedings of the 38th\nInternational Conference on Machine Learning, ICML 2021, 18-24 July\n2021, Virtual Event, edited by Marina Meila and Tong Zhang,\n139:5637–64. Proceedings of Machine Learning Research. PMLR. http://proceedings.mlr.press/v139/koh21a.html.\n\n\nKoren, Yehuda, Robert Bell, and Chris Volinsky. 2009. “Matrix\nFactorization Techniques for Recommender Systems.”\nComputer 42 (8): 30–37. https://doi.org/10.1109/mc.2009.263.\n\n\nKrishna, Adithya, Srikanth Rohit Nudurupati, Chandana D G, Pritesh\nDwivedi, André van Schaik, Mahesh Mehendale, and Chetan Singh Thakur.\n2023. “RAMAN: A Re-Configurable and\nSparse TinyML Accelerator for Inference on Edge.” https://arxiv.org/abs/2306.06493.\n\n\nKrishnamoorthi. 2018. “Quantizing Deep Convolutional Networks for\nEfficient Inference: A Whitepaper.” ArXiv\nPreprint. https://arxiv.org/abs/1806.08342.\n\n\nKrishnan, Rayan, Pranav Rajpurkar, and Eric J. Topol. 2022.\n“Self-Supervised Learning in Medicine and Healthcare.”\nNat. Biomed. Eng. 6 (12): 1346–52. https://doi.org/10.1038/s41551-022-00914-1.\n\n\nKrishnan, Srivatsan, Natasha Jaques, Shayegan Omidshafiei, Dan Zhang,\nIzzeddin Gur, Vijay Janapa Reddi, and Aleksandra Faust. 2022.\n“Multi-Agent Reinforcement Learning for Microprocessor Design\nSpace Exploration.” https://arxiv.org/abs/2211.16385.\n\n\nKrishnan, Srivatsan, Amir Yazdanbakhsh, Shvetank Prakash, Jason Jabbour,\nIkechukwu Uchendu, Susobhan Ghosh, Behzad Boroujerdian, et al. 2023.\n“ArchGym: An Open-Source Gymnasium for\nMachine Learning Assisted Architecture Design.” In\nProceedings of the 50th Annual International Symposium on Computer\nArchitecture, 1–16. ACM. https://doi.org/10.1145/3579371.3589049.\n\n\nKrizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. 2012.\n“ImageNet Classification with Deep Convolutional\nNeural Networks.” In Advances in Neural Information\nProcessing Systems 25: 26th Annual Conference on Neural Information\nProcessing Systems 2012. Proceedings of a Meeting Held December 3-6,\n2012, Lake Tahoe, Nevada, United States, edited by Peter L.\nBartlett, Fernando C. N. Pereira, Christopher J. C. Burges, Léon Bottou,\nand Kilian Q. Weinberger, 1106–14. https://proceedings.neurips.cc/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html.\n\n\n———. 2017. “ImageNet Classification with Deep\nConvolutional Neural Networks.” Edited by F. Pereira, C. J.\nBurges, L. Bottou, and K. Q. Weinberger. Commun. ACM 60 (6):\n84–90. https://doi.org/10.1145/3065386.\n\n\nKung, Hsiang Tsung, and Charles E Leiserson. 1979. “Systolic\nArrays (for VLSI).” In Sparse Matrix Proceedings\n1978, 1:256–82. Society for industrial; applied mathematics\nPhiladelphia, PA, USA.\n\n\nKurth, Thorsten, Shashank Subramanian, Peter Harrington, Jaideep Pathak,\nMorteza Mardani, David Hall, Andrea Miele, Karthik Kashinath, and Anima\nAnandkumar. 2023. “FourCastNet:\nAccelerating Global High-Resolution Weather Forecasting\nUsing Adaptive Fourier Neural Operators.” In\nProceedings of the Platform for Advanced Scientific Computing\nConference, 1–11. ACM. https://doi.org/10.1145/3592979.3593412.\n\n\nKuzmin, Andrey, Mart Van Baalen, Yuwei Ren, Markus Nagel, Jorn Peters,\nand Tijmen Blankevoort. 2022. “FP8 Quantization:\nThe Power of the Exponent.” https://arxiv.org/abs/2208.09225.\n\n\nKuznetsova, Alina, Hassan Rom, Neil Alldrin, Jasper Uijlings, Ivan\nKrasin, Jordi Pont-Tuset, Shahab Kamali, et al. 2020. “The Open\nImages Dataset V4: Unified Image Classification, Object\nDetection, and Visual Relationship Detection at Scale.”\nInternational Journal of Computer Vision 128 (7): 1956–81.\n\n\nKwon, Jisu, and Daejin Park. 2021. “Hardware/Software\nCo-Design for TinyML Voice-Recognition Application on\nResource Frugal Edge Devices.” Applied Sciences 11 (22):\n11073. https://doi.org/10.3390/app112211073.\n\n\nKwon, Sun Hwa, and Lin Dong. 2022. “Flexible Sensors and Machine\nLearning for Heart Monitoring.” Nano Energy 102\n(November): 107632. https://doi.org/10.1016/j.nanoen.2022.107632.\n\n\nKwon, Young D, Rui Li, Stylianos I Venieris, Jagmohan Chauhan, Nicholas\nD Lane, and Cecilia Mascolo. 2023. “TinyTrain:\nDeep Neural Network Training at the Extreme Edge.”\nArXiv Preprint abs/2307.09988. https://arxiv.org/abs/2307.09988.\n\n\nLai, Liangzhen, Naveen Suda, and Vikas Chandra. 2018a. “Cmsis-Nn:\nEfficient Neural Network Kernels for Arm Cortex-m\nCpus.” ArXiv Preprint abs/1801.06601. https://arxiv.org/abs/1801.06601.\n\n\n———. 2018b. “CMSIS-NN:\nEfficient Neural Network Kernels for Arm Cortex-m\nCPUs.” https://arxiv.org/abs/1801.06601.\n\n\nLakkaraju, Himabindu, and Osbert Bastani. 2020.\n“”How Do i Fool You?”:\nManipulating User Trust via Misleading Black Box Explanations.”\nIn Proceedings of the AAAI/ACM Conference on AI, Ethics, and\nSociety, 79–85. ACM. https://doi.org/10.1145/3375627.3375833.\n\n\nLam, Remi, Alvaro Sanchez-Gonzalez, Matthew Willson, Peter Wirnsberger,\nMeire Fortunato, Ferran Alet, Suman Ravuri, et al. 2023. “Learning\nSkillful Medium-Range Global Weather Forecasting.”\nScience 382 (6677): 1416–21. https://doi.org/10.1126/science.adi2336.\n\n\nLannelongue, Loı̈c, Jason Grealey, and Michael Inouye. 2021. “Green\nAlgorithms: Quantifying the Carbon Footprint of\nComputation.” Adv. Sci. 8 (12): 2100707. https://doi.org/10.1002/advs.202100707.\n\n\nLeCun, Yann, John Denker, and Sara Solla. 1989. “Optimal Brain\nDamage.” Adv Neural Inf Process Syst 2.\n\n\nLee, Minwoong, Namho Lee, Huijeong Gwon, Jongyeol Kim, Younggwan Hwang,\nand Seongik Cho. 2022. “Design of Radiation-Tolerant High-Speed\nSignal Processing Circuit for Detecting Prompt Gamma Rays by Nuclear\nExplosion.” Electronics 11 (18): 2970. https://doi.org/10.3390/electronics11182970.\n\n\nLeRoy Poff, N, MM Brinson, and JW Day. 2002. “Aquatic Ecosystems\n& Global Climate Change.” Pew Center on Global Climate\nChange.\n\n\nLi, En, Liekang Zeng, Zhi Zhou, and Xu Chen. 2020. “Edge\nAI: On-demand Accelerating Deep\nNeural Network Inference via Edge Computing.” IEEE Trans.\nWireless Commun. 19 (1): 447–57. https://doi.org/10.1109/twc.2019.2946140.\n\n\nLi, Guanpeng, Siva Kumar Sastry Hari, Michael Sullivan, Timothy Tsai,\nKarthik Pattabiraman, Joel Emer, and Stephen W. Keckler. 2017.\n“Understanding Error Propagation in Deep Learning Neural Network\n(DNN) Accelerators and Applications.” In\nProceedings of the International Conference for High Performance\nComputing, Networking, Storage and Analysis, 1–12. ACM. https://doi.org/10.1145/3126908.3126964.\n\n\nLi, Jingzhen, Igbe Tobore, Yuhang Liu, Abhishek Kandwal, Lei Wang, and\nZedong Nie. 2021. “Non-Invasive Monitoring of Three Glucose Ranges\nBased on ECG by Using\nDBSCAN-CNN.” #IEEE_J_BHI# 25\n(9): 3340–50. https://doi.org/10.1109/jbhi.2021.3072628.\n\n\nLi, Mu, David G. Andersen, Alexander J. Smola, and Kai Yu. 2014.\n“Communication Efficient Distributed Machine Learning with the\nParameter Server.” In Advances in Neural Information\nProcessing Systems 27: Annual Conference on Neural Information\nProcessing Systems 2014, December 8-13 2014, Montreal, Quebec,\nCanada, edited by Zoubin Ghahramani, Max Welling, Corinna Cortes,\nNeil D. Lawrence, and Kilian Q. Weinberger, 19–27. https://proceedings.neurips.cc/paper/2014/hash/1ff1de774005f8da13f42943881c655f-Abstract.html.\n\n\nLi, Qinbin, Zeyi Wen, Zhaomin Wu, Sixu Hu, Naibo Wang, Yuan Li, Xu Liu,\nand Bingsheng He. 2023. “A Survey on Federated Learning Systems:\nVision, Hype and Reality for Data Privacy and\nProtection.” IEEE Trans. Knowl. Data Eng. 35 (4):\n3347–66. https://doi.org/10.1109/tkde.2021.3124599.\n\n\nLi, Tian, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith. 2020.\n“Federated Learning: Challenges, Methods, and Future\nDirections.” IEEE Signal Process Mag. 37 (3): 50–60. https://doi.org/10.1109/msp.2020.2975749.\n\n\nLi, Xiang, Tao Qin, Jian Yang, and Tie-Yan Liu. 2016.\n“LightRNN: Memory and\nComputation-Efficient Recurrent Neural Networks.” In Advances\nin Neural Information Processing Systems 29: Annual Conference on Neural\nInformation Processing Systems 2016, December 5-10, 2016, Barcelona,\nSpain, edited by Daniel D. Lee, Masashi Sugiyama, Ulrike von\nLuxburg, Isabelle Guyon, and Roman Garnett, 4385–93. https://proceedings.neurips.cc/paper/2016/hash/c3e4035af2a1cde9f21e1ae1951ac80b-Abstract.html.\n\n\nLi, Yuhang, Xin Dong, and Wei Wang. 2020. “Additive Powers-of-Two\nQuantization: An Efficient Non-Uniform Discretization for\nNeural Networks.” In 8th International Conference on Learning\nRepresentations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30,\n2020. OpenReview.net. https://openreview.net/forum?id=BkgXT24tDS.\n\n\nLi, Zhizhong, and Derek Hoiem. 2018. “Learning Without\nForgetting.” IEEE Trans. Pattern Anal. Mach. Intell. 40\n(12): 2935–47. https://doi.org/10.1109/tpami.2017.2773081.\n\n\nLin, Ji, Wei-Ming Chen, Yujun Lin, John Cohn, Chuang Gan, and Song Han.\n2020. “MCUNet: Tiny Deep Learning on\nIoT Devices.” In Advances in Neural Information\nProcessing Systems 33: Annual Conference on Neural Information\nProcessing Systems 2020, NeurIPS 2020, December 6-12, 2020,\nVirtual, edited by Hugo Larochelle, Marc’Aurelio Ranzato, Raia\nHadsell, Maria-Florina Balcan, and Hsuan-Tien Lin. https://proceedings.neurips.cc/paper/2020/hash/86c51678350f656dcc7f490a43946ee5-Abstract.html.\n\n\nLin, Ji, Ligeng Zhu, Wei-Ming Chen, Wei-Chen Wang, Chuang Gan, and Song\nHan. 2022. “On-Device Training Under 256kb Memory.”\nAdv. Neur. In. 35: 22941–54.\n\n\nLin, Ji, Ligeng Zhu, Wei-Ming Chen, Wei-Chen Wang, and Song Han. 2023.\n“Tiny Machine Learning: Progress and Futures Feature.”\nIEEE Circuits Syst. Mag. 23 (3): 8–34. https://doi.org/10.1109/mcas.2023.3302182.\n\n\nLin, Tsung-Yi, Michael Maire, Serge Belongie, James Hays, Pietro Perona,\nDeva Ramanan, Piotr Dollár, and C Lawrence Zitnick. 2014.\n“Microsoft Coco: Common Objects in Context.”\nIn Computer VisionECCV 2014: 13th European Conference,\nZurich, Switzerland, September 6-12, 2014, Proceedings, Part v 13,\n740–55. Springer.\n\n\nLindgren, Simon. 2023. Handbook of Critical Studies of Artificial\nIntelligence. Edward Elgar Publishing.\n\n\nLindholm, Andreas, Dave Zachariah, Petre Stoica, and Thomas B. Schon.\n2019. “Data Consistency Approach to Model Validation.”\n#IEEE_O_ACC# 7: 59788–96. https://doi.org/10.1109/access.2019.2915109.\n\n\nLindholm, Erik, John Nickolls, Stuart Oberman, and John Montrym. 2008.\n“NVIDIA Tesla: A Unified Graphics and\nComputing Architecture.” IEEE Micro 28 (2): 39–55. https://doi.org/10.1109/mm.2008.31.\n\n\nLin, Tang Tang, Dang Yang, and Han Gan. 2023. “AWQ:\nActivation-aware Weight Quantization for\nLLM Compression and Acceleration.” ArXiv\nPreprint. https://arxiv.org/abs/2306.00978.\n\n\nLiu, Yanan, Xiaoxia Wei, Jinyu Xiao, Zhijie Liu, Yang Xu, and Yun Tian.\n2020. “Energy Consumption and Emission Mitigation Prediction Based\non Data Center Traffic and PUE for Global Data\nCenters.” Global Energy Interconnection 3 (3): 272–82.\nhttps://doi.org/10.1016/j.gloei.2020.07.008.\n\n\nLiu, Yingcheng, Guo Zhang, Christopher G. Tarolli, Rumen Hristov, Stella\nJensen-Roberts, Emma M. Waddell, Taylor L. Myers, et al. 2022.\n“Monitoring Gait at Home with Radio Waves in\nParkinson’s Disease: A Marker of Severity,\nProgression, and Medication Response.” Sci. Transl. Med.\n14 (663): eadc9669. https://doi.org/10.1126/scitranslmed.adc9669.\n\n\nLoh, Gabriel H. 2008. “3D-Stacked Memory\nArchitectures for Multi-Core Processors.” ACM SIGARCH\nComputer Architecture News 36 (3): 453–64. https://doi.org/10.1145/1394608.1382159.\n\n\nLopez-Paz, David, and Marc’Aurelio Ranzato. 2017. “Gradient\nEpisodic Memory for Continual Learning.” Adv Neural Inf\nProcess Syst 30.\n\n\nLou, Yin, Rich Caruana, Johannes Gehrke, and Giles Hooker. 2013.\n“Accurate Intelligible Models with Pairwise Interactions.”\nIn Proceedings of the 19th ACM SIGKDD International Conference on\nKnowledge Discovery and Data Mining, edited by Inderjit S. Dhillon,\nYehuda Koren, Rayid Ghani, Ted E. Senator, Paul Bradley, Rajesh Parekh,\nJingrui He, Robert L. Grossman, and Ramasamy Uthurusamy, 623–31. ACM. https://doi.org/10.1145/2487575.2487579.\n\n\nLowy, Andrew, Rakesh Pavan, Sina Baharlouei, Meisam Razaviyayn, and\nAhmad Beirami. 2021. “Fermi: Fair Empirical Risk\nMinimization via Exponential Rényi Mutual Information.”\n\n\nLubana, Ekdeep Singh, and Robert P Dick. 2020. “A Gradient Flow\nFramework for Analyzing Network Pruning.” arXiv Preprint\narXiv:2009.11839.\n\n\nLuebke, David. 2008. “CUDA: Scalable\nParallel Programming for High-Performance Scientific Computing.”\nIn 2008 5th IEEE International Symposium on Biomedical Imaging: From\nNano to Macro, 836–38. IEEE. https://doi.org/10.1109/isbi.2008.4541126.\n\n\nLundberg, Scott M., and Su-In Lee. 2017. “A Unified Approach to\nInterpreting Model Predictions.” In Advances in Neural\nInformation Processing Systems 30: Annual Conference on Neural\nInformation Processing Systems 2017, December 4-9, 2017, Long Beach, CA,\nUSA, edited by Isabelle Guyon, Ulrike von Luxburg, Samy Bengio,\nHanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett,\n4765–74. https://proceedings.neurips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html.\n\n\nMa, Dongning, Fred Lin, Alban Desmaison, Joel Coburn, Daniel Moore,\nSriram Sankar, and Xun Jiao. 2024. “Dr.\nDNA: Combating Silent Data Corruptions in Deep\nLearning Using Distribution of Neuron Activations.” In\nProceedings of the 29th ACM International Conference on\nArchitectural Support for Programming Languages and Operating Systems,\nVolume 3, 239–52. ACM. https://doi.org/10.1145/3620666.3651349.\n\n\nMaas, Martin, David G. Andersen, Michael Isard, Mohammad Mahdi\nJavanmard, Kathryn S. McKinley, and Colin Raffel. 2024. “Combining\nMachine Learning and Lifetime-Based Resource Management for Memory\nAllocation and Beyond.” Commun. ACM 67 (4): 87–96. https://doi.org/10.1145/3611018.\n\n\nMaass, Wolfgang. 1997. “Networks of Spiking Neurons:\nThe Third Generation of Neural Network Models.”\nNeural Networks 10 (9): 1659–71. https://doi.org/10.1016/s0893-6080(97)00011-7.\n\n\nMadry, Aleksander, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras,\nand Adrian Vladu. 2017. “Towards Deep Learning Models Resistant to\nAdversarial Attacks.” arXiv Preprint arXiv:1706.06083.\n\n\nMahmoud, Abdulrahman, Neeraj Aggarwal, Alex Nobbe, Jose Rodrigo Sanchez\nVicarte, Sarita V. Adve, Christopher W. Fletcher, Iuri Frosio, and Siva\nKumar Sastry Hari. 2020. “PyTorchFI: A\nRuntime Perturbation Tool for DNNs.” In 2020\n50th Annual IEEE/IFIP International Conference on Dependable Systems and\nNetworks Workshops (DSN-w), 25–31. IEEE; IEEE. https://doi.org/10.1109/dsn-w50199.2020.00014.\n\n\nMahmoud, Abdulrahman, Siva Kumar Sastry Hari, Christopher W. Fletcher,\nSarita V. Adve, Charbel Sakr, Naresh Shanbhag, Pavlo Molchanov, Michael\nB. Sullivan, Timothy Tsai, and Stephen W. Keckler. 2021.\n“Optimizing Selective Protection for CNN\nResilience.” In 2021 IEEE 32nd International Symposium on\nSoftware Reliability Engineering (ISSRE), 127–38. IEEE. https://doi.org/10.1109/issre52982.2021.00025.\n\n\nMahmoud, Abdulrahman, Thierry Tambe, Tarek Aloui, David Brooks, and\nGu-Yeon Wei. 2022. “GoldenEye: A\nPlatform for Evaluating Emerging Numerical Data Formats in\nDNN Accelerators.” In 2022 52nd Annual IEEE/IFIP\nInternational Conference on Dependable Systems and Networks (DSN),\n206–14. IEEE. https://doi.org/10.1109/dsn53405.2022.00031.\n\n\nMarković, Danijela, Alice Mizrahi, Damien Querlioz, and Julie Grollier.\n2020. “Physics for Neuromorphic Computing.” Nature\nReviews Physics 2 (9): 499–510. https://doi.org/10.1038/s42254-020-0208-2.\n\n\nMartin, C. Dianne. 1993. “The Myth of the Awesome Thinking\nMachine.” Commun. ACM 36 (4): 120–33. https://doi.org/10.1145/255950.153587.\n\n\nMarulli, Fiammetta, Stefano Marrone, and Laura Verde. 2022.\n“Sensitivity of Machine Learning Approaches to Fake and Untrusted\nData in Healthcare Domain.” Journal of Sensor and Actuator\nNetworks 11 (2): 21. https://doi.org/10.3390/jsan11020021.\n\n\nMaslej, Nestor, Loredana Fattorini, Erik Brynjolfsson, John Etchemendy,\nKatrina Ligett, Terah Lyons, James Manyika, et al. 2023.\n“Artificial Intelligence Index Report 2023.” ArXiv\nPreprint abs/2310.03715. https://arxiv.org/abs/2310.03715.\n\n\nMattson, Peter, Vijay Janapa Reddi, Christine Cheng, Cody Coleman, Greg\nDiamos, David Kanter, Paulius Micikevicius, et al. 2020a.\n“MLPerf: An Industry Standard Benchmark\nSuite for Machine Learning Performance.” IEEE Micro 40\n(2): 8–16. https://doi.org/10.1109/mm.2020.2974843.\n\n\n———, et al. 2020b. “MLPerf: An Industry\nStandard Benchmark Suite for Machine Learning Performance.”\nIEEE Micro 40 (2): 8–16. https://doi.org/10.1109/mm.2020.2974843.\n\n\nMazumder, Mark, Sharad Chitlangia, Colby Banbury, Yiping Kang, Juan\nManuel Ciro, Keith Achorn, Daniel Galvez, et al. 2021.\n“Multilingual Spoken Words Corpus.” In Thirty-Fifth\nConference on Neural Information Processing Systems Datasets and\nBenchmarks Track (Round 2).\n\n\nMcCarthy, John. 1981. “Epistemological Problems of Artificial\nIntelligence.” In Readings in Artificial Intelligence,\n459–65. Elsevier. https://doi.org/10.1016/b978-0-934613-03-3.50035-0.\n\n\nMcMahan, Brendan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise\nAgüera y Arcas. 2017. “Communication-Efficient Learning of Deep\nNetworks from Decentralized Data.” In Proceedings of the 20th\nInternational Conference on Artificial Intelligence and Statistics,\nAISTATS 2017, 20-22 April 2017, Fort Lauderdale, FL, USA, edited by\nAarti Singh and Xiaojin (Jerry) Zhu, 54:1273–82. Proceedings of Machine\nLearning Research. PMLR. http://proceedings.mlr.press/v54/mcmahan17a.html.\n\n\nMiller, Charlie. 2019. “Lessons Learned from Hacking a\nCar.” IEEE Design &Amp; Test 36 (6): 7–9. https://doi.org/10.1109/mdat.2018.2863106.\n\n\nMiller, Charlie, and Chris Valasek. 2015. “Remote Exploitation of\nan Unaltered Passenger Vehicle.” Black Hat USA 2015 (S\n91): 1–91.\n\n\nMiller, D. A. B. 2000. “Optical Interconnects to Silicon.”\n#IEEE_J_JSTQE# 6 (6): 1312–17. https://doi.org/10.1109/2944.902184.\n\n\nMills, Andrew, and Stephen Le Hunte. 1997. “An Overview of\nSemiconductor Photocatalysis.” J. Photochem. Photobiol.,\nA 108 (1): 1–35. https://doi.org/10.1016/s1010-6030(97)00118-4.\n\n\nMirhoseini, Azalia, Anna Goldie, Mustafa Yazgan, Joe Wenjie Jiang,\nEbrahim Songhori, Shen Wang, Young-Joon Lee, et al. 2021. “A Graph\nPlacement Methodology for Fast Chip Design.” Nature 594\n(7862): 207–12. https://doi.org/10.1038/s41586-021-03544-w.\n\n\nMishra, Asit K., Jorge Albericio Latorre, Jeff Pool, Darko Stosic, Dusan\nStosic, Ganesh Venkatesh, Chong Yu, and Paulius Micikevicius. 2021.\n“Accelerating Sparse Deep Neural Networks.” CoRR\nabs/2104.08378. https://arxiv.org/abs/2104.08378.\n\n\nMittal, Sparsh, Gaurav Verma, Brajesh Kaushik, and Farooq A. Khanday.\n2021. “A Survey of SRAM-Based in-Memory Computing\nTechniques and Applications.” J. Syst. Architect. 119\n(October): 102276. https://doi.org/10.1016/j.sysarc.2021.102276.\n\n\nModha, Dharmendra S., Filipp Akopyan, Alexander Andreopoulos,\nRathinakumar Appuswamy, John V. Arthur, Andrew S. Cassidy, Pallab Datta,\net al. 2023. “Neural Inference at the Frontier of Energy, Space,\nand Time.” Science 382 (6668): 329–35. https://doi.org/10.1126/science.adh1174.\n\n\nMohanram, K., and N. A. Touba. 2003. “Partial Error Masking to\nReduce Soft Error Failure Rate in Logic Circuits.” In\nProceedings. 16th IEEE Symposium on Computer Arithmetic,\n433–40. IEEE; IEEE Comput. Soc. https://doi.org/10.1109/dftvs.2003.1250141.\n\n\nMonyei, Chukwuka G., and Kirsten E. H. Jenkins. 2018. “Electrons\nHave No Identity: Setting Right Misrepresentations in\nGoogle and Apple’s Clean Energy Purchasing.”\nEnergy Research &Amp; Social Science 46 (December): 48–51.\nhttps://doi.org/10.1016/j.erss.2018.06.015.\n\n\nMoshawrab, Mohammad, Mehdi Adda, Abdenour Bouzouane, Hussein Ibrahim,\nand Ali Raad. 2023. “Reviewing Federated Learning Aggregation\nAlgorithms; Strategies, Contributions, Limitations and Future\nPerspectives.” Electronics 12 (10): 2287. https://doi.org/10.3390/electronics12102287.\n\n\nMukherjee, S. S., J. Emer, and S. K. Reinhardt. 2005. “The Soft\nError Problem: An Architectural Perspective.” In\n11th International Symposium on High-Performance Computer\nArchitecture, 243–47. IEEE; IEEE. https://doi.org/10.1109/hpca.2005.37.\n\n\nMunshi, Aaftab. 2009. “The OpenCL\nSpecification.” In 2009 IEEE Hot Chips 21 Symposium\n(HCS), 1–314. IEEE. https://doi.org/10.1109/hotchips.2009.7478342.\n\n\nMusk, Elon et al. 2019. “An Integrated Brain-Machine Interface\nPlatform with Thousands of Channels.” J. Med. Internet\nRes. 21 (10): e16194. https://doi.org/10.2196/16194.\n\n\nMyllyaho, Lalli, Mikko Raatikainen, Tomi Männistö, Jukka K. Nurminen,\nand Tommi Mikkonen. 2022. “On Misbehaviour and Fault Tolerance in\nMachine Learning Systems.” J. Syst. Software 183\n(January): 111096. https://doi.org/10.1016/j.jss.2021.111096.\n\n\nNakano, Jane. 2021. The Geopolitics of Critical Minerals Supply\nChains. JSTOR.\n\n\nNarayanan, Arvind, and Vitaly Shmatikov. 2006. “How to Break\nAnonymity of the Netflix Prize Dataset.” arXiv Preprint\nCs/0610105.\n\n\nNg, Davy Tsz Kit, Jac Ka Lok Leung, Kai Wah Samuel Chu, and Maggie Shen\nQiao. 2021. “AI Literacy: Definition,\nTeaching, Evaluation and Ethical Issues.” Proceedings of the\nAssociation for Information Science and Technology 58 (1): 504–9.\n\n\nNgo, Richard, Lawrence Chan, and Sören Mindermann. 2022. “The\nAlignment Problem from a Deep Learning Perspective.” ArXiv\nPreprint abs/2209.00626. https://arxiv.org/abs/2209.00626.\n\n\nNguyen, Ngoc-Bao, Keshigeyan Chandrasegaran, Milad Abdollahzadeh, and\nNgai-Man Cheung. 2023. “Re-Thinking Model Inversion Attacks\nAgainst Deep Neural Networks.” In 2023 IEEE/CVF Conference on\nComputer Vision and Pattern Recognition (CVPR), 16384–93. IEEE. https://doi.org/10.1109/cvpr52729.2023.01572.\n\n\nNorrie, Thomas, Nishant Patil, Doe Hyun Yoon, George Kurian, Sheng Li,\nJames Laudon, Cliff Young, Norman Jouppi, and David Patterson. 2021.\n“The Design Process for Google’s Training Chips:\nTpuv2 and TPUv3.” IEEE Micro\n41 (2): 56–63. https://doi.org/10.1109/mm.2021.3058217.\n\n\nNorthcutt, Curtis G, Anish Athalye, and Jonas Mueller. 2021.\n“Pervasive Label Errors in Test Sets Destabilize Machine Learning\nBenchmarks.” arXiv. https://doi.org/https://doi.org/10.48550/arXiv.2103.14749\narXiv-issued DOI via DataCite.\n\n\nObermeyer, Ziad, Brian Powers, Christine Vogeli, and Sendhil\nMullainathan. 2019. “Dissecting Racial Bias in an Algorithm Used\nto Manage the Health of Populations.” Science 366\n(6464): 447–53. https://doi.org/10.1126/science.aax2342.\n\n\nOecd. 2023. “A Blueprint for Building National Compute Capacity\nfor Artificial Intelligence.” 350. Organisation for Economic\nCo-Operation; Development (OECD). https://doi.org/10.1787/876367e3-en.\n\n\nOlah, Chris, Nick Cammarata, Ludwig Schubert, Gabriel Goh, Michael\nPetrov, and Shan Carter. 2020. “Zoom in: An\nIntroduction to Circuits.” Distill 5 (3): e00024–001. https://doi.org/10.23915/distill.00024.001.\n\n\nOliynyk, Daryna, Rudolf Mayer, and Andreas Rauber. 2023. “I Know\nWhat You Trained Last Summer: A Survey on Stealing Machine\nLearning Models and Defences.” ACM Comput. Surv. 55\n(14s): 1–41. https://doi.org/10.1145/3595292.\n\n\nOoko, Samson Otieno, Marvin Muyonga Ogore, Jimmy Nsenga, and Marco\nZennaro. 2021. “TinyML in Africa:\nOpportunities and Challenges.” In 2021 IEEE\nGlobecom Workshops (GC Wkshps), 1–6. IEEE; IEEE. https://doi.org/10.1109/gcwkshps52748.2021.9682107.\n\n\nOprea, Alina, Anoop Singhal, and Apostol Vassilev. 2022.\n“Poisoning Attacks Against Machine Learning: Can\nMachine Learning Be Trustworthy?” Computer 55 (11):\n94–99. https://doi.org/10.1109/mc.2022.3190787.\n\n\nPan, Sinno Jialin, and Qiang Yang. 2010. “A Survey on Transfer\nLearning.” IEEE Trans. Knowl. Data Eng. 22 (10):\n1345–59. https://doi.org/10.1109/tkde.2009.191.\n\n\nPanda, Priyadarshini, Indranil Chakraborty, and Kaushik Roy. 2019.\n“Discretization Based Solutions for Secure Machine Learning\nAgainst Adversarial Attacks.” #IEEE_O_ACC# 7: 70157–68.\nhttps://doi.org/10.1109/access.2019.2919463.\n\n\nPapadimitriou, George, and Dimitris Gizopoulos. 2021.\n“Demystifying the System Vulnerability Stack:\nTransient Fault Effects Across the Layers.” In\n2021 ACM/IEEE 48th Annual International Symposium on Computer\nArchitecture (ISCA), 902–15. IEEE; IEEE. https://doi.org/10.1109/isca52012.2021.00075.\n\n\nPapernot, Nicolas, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram\nSwami. 2016. “Distillation as a Defense to Adversarial\nPerturbations Against Deep Neural Networks.” In 2016 IEEE\nSymposium on Security and Privacy (SP), 582–97. IEEE; IEEE. https://doi.org/10.1109/sp.2016.41.\n\n\nParrish, Alicia, Hannah Rose Kirk, Jessica Quaye, Charvi Rastogi, Max\nBartolo, Oana Inel, Juan Ciro, et al. 2023. “Adversarial Nibbler:\nA Data-Centric Challenge for Improving the Safety of\nText-to-Image Models.” ArXiv Preprint abs/2305.14384. https://arxiv.org/abs/2305.14384.\n\n\nPatterson, David A, and John L Hennessy. 2016. Computer Organization\nand Design ARM Edition: The Hardware Software\nInterface. Morgan kaufmann.\n\n\nPatterson, David, Joseph Gonzalez, Urs Holzle, Quoc Le, Chen Liang,\nLluis-Miquel Munguia, Daniel Rothchild, David R. So, Maud Texier, and\nJeff Dean. 2022. “The Carbon Footprint of Machine Learning\nTraining Will Plateau, Then Shrink.” Computer 55 (7):\n18–28. https://doi.org/10.1109/mc.2022.3148714.\n\n\nPeters, Dorian, Rafael A. Calvo, and Richard M. Ryan. 2018.\n“Designing for Motivation, Engagement and Wellbeing in Digital\nExperience.” Front. Psychol. 9 (May): 797. https://doi.org/10.3389/fpsyg.2018.00797.\n\n\nPhillips, P Jonathon, Carina A Hahn, Peter C Fontana, David A\nBroniatowski, and Mark A Przybocki. 2020. “Four Principles of\nExplainable Artificial Intelligence.” Gaithersburg,\nMaryland 18.\n\n\nPlank, James S. 1997. “A Tutorial on\nReedSolomon Coding for Fault-Tolerance in\nRAID-Like Systems.” Software: Practice and\nExperience 27 (9): 995–1012.\n\n\nPont, Michael J, and Royan HL Ong. 2002. “Using Watchdog Timers to\nImprove the Reliability of Single-Processor Embedded Systems:\nSeven New Patterns and a Case Study.” In\nProceedings of the First Nordic Conference on Pattern Languages of\nPrograms, 159–200. Citeseer.\n\n\nPrakash, Shvetank, Tim Callahan, Joseph Bushagour, Colby Banbury, Alan\nV. Green, Pete Warden, Tim Ansell, and Vijay Janapa Reddi. 2023.\n“CFU Playground: Full-stack Open-Source Framework for Tiny Machine\nLearning (TinyML) Acceleration on\nFPGAs.” In 2023 IEEE International Symposium on\nPerformance Analysis of Systems and Software (ISPASS). Vol.\nabs/2201.01863. IEEE. https://doi.org/10.1109/ispass57527.2023.00024.\n\n\nPrakash, Shvetank, Matthew Stewart, Colby Banbury, Mark Mazumder, Pete\nWarden, Brian Plancher, and Vijay Janapa Reddi. 2023. “Is\nTinyML Sustainable? Assessing the Environmental Impacts of\nMachine Learning on Microcontrollers.” ArXiv Preprint.\nhttps://arxiv.org/abs/2301.11899.\n\n\nPsoma, Sotiria D., and Chryso Kanthou. 2023. “Wearable Insulin\nBiosensors for Diabetes Management: Advances and\nChallenges.” Biosensors 13 (7): 719. https://doi.org/10.3390/bios13070719.\n\n\nPushkarna, Mahima, Andrew Zaldivar, and Oddur Kjartansson. 2022.\n“Data Cards: Purposeful and Transparent Dataset\nDocumentation for Responsible AI.” In 2022 ACM\nConference on Fairness, Accountability, and Transparency. ACM. https://doi.org/10.1145/3531146.3533231.\n\n\nPutnam, Andrew, Adrian M. Caulfield, Eric S. Chung, Derek Chiou, Kypros\nConstantinides, John Demme, Hadi Esmaeilzadeh, et al. 2014. “A\nReconfigurable Fabric for Accelerating Large-Scale Datacenter\nServices.” ACM SIGARCH Computer Architecture News 42\n(3): 13–24. https://doi.org/10.1145/2678373.2665678.\n\n\nQi, Chen, Shibo Shen, Rongpeng Li, Zhifeng Zhao, Qing Liu, Jing Liang,\nand Honggang Zhang. 2021. “An Efficient Pruning Scheme of Deep\nNeural Networks for Internet of Things Applications.” EURASIP\nJournal on Advances in Signal Processing 2021 (1): 31. https://doi.org/10.1186/s13634-021-00744-4.\n\n\nQian, Yu, Xuegong Zhou, Hao Zhou, and Lingli Wang. 2024. “An\nEfficient Reinforcement Learning Based Framework for Exploring Logic\nSynthesis.” ACM Trans. Des. Autom. Electron. Syst. 29\n(2): 1–33. https://doi.org/10.1145/3632174.\n\n\nR. V., Rashmi, and Karthikeyan A. 2018. “Secure Boot of Embedded\nApplications - a Review.” In 2018 Second International\nConference on Electronics, Communication and Aerospace Technology\n(ICECA), 291–98. IEEE. https://doi.org/10.1109/iceca.2018.8474730.\n\n\nRachwan, John, Daniel Zügner, Bertrand Charpentier, Simon Geisler,\nMorgane Ayle, and Stephan Günnemann. 2022. “Winning the Lottery\nAhead of Time: Efficient Early Network Pruning.” In\nInternational Conference on Machine Learning, 18293–309. PMLR.\n\n\nRaina, Rajat, Anand Madhavan, and Andrew Y. Ng. 2009. “Large-Scale\nDeep Unsupervised Learning Using Graphics Processors.” In\nProceedings of the 26th Annual International Conference on Machine\nLearning, edited by Andrea Pohoreckyj Danyluk, Léon Bottou, and\nMichael L. Littman, 382:873–80. ACM International Conference Proceeding\nSeries. ACM. https://doi.org/10.1145/1553374.1553486.\n\n\nRamaswamy, Vikram V., Sunnie S. Y. Kim, Ruth Fong, and Olga Russakovsky.\n2023a. “Overlooked Factors in Concept-Based Explanations:\nDataset Choice, Concept Learnability, and Human\nCapability.” In 2023 IEEE/CVF Conference on Computer Vision\nand Pattern Recognition (CVPR), 10932–41. IEEE. https://doi.org/10.1109/cvpr52729.2023.01052.\n\n\nRamaswamy, Vikram V, Sunnie SY Kim, Ruth Fong, and Olga Russakovsky.\n2023b. “UFO: A Unified Method for\nControlling Understandability and Faithfulness Objectives in\nConcept-Based Explanations for CNNs.” ArXiv\nPreprint abs/2303.15632. https://arxiv.org/abs/2303.15632.\n\n\nRamcharan, Amanda, Kelsee Baranowski, Peter McCloskey, Babuali Ahmed,\nJames Legg, and David P. Hughes. 2017. “Deep Learning for\nImage-Based Cassava Disease Detection.” Front. Plant\nSci. 8 (October): 1852. https://doi.org/10.3389/fpls.2017.01852.\n\n\nRamesh, Aditya, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss,\nAlec Radford, Mark Chen, and Ilya Sutskever. 2021. “Zero-Shot\nText-to-Image Generation.” In Proceedings of the 38th\nInternational Conference on Machine Learning, ICML 2021, 18-24 July\n2021, Virtual Event, edited by Marina Meila and Tong Zhang,\n139:8821–31. Proceedings of Machine Learning Research. PMLR. http://proceedings.mlr.press/v139/ramesh21a.html.\n\n\nRanganathan, Parthasarathy. 2011. “From Microprocessors to\nNanostores: Rethinking Data-Centric Systems.”\nComputer 44 (1): 39–48. https://doi.org/10.1109/mc.2011.18.\n\n\nRao, Ravi. 2021. “TinyML Unlocks New Possibilities\nfor Sustainable Development Technologies.”\nWww.wevolver.com. https://www.wevolver.com/article/tinyml-unlocks-new-possibilities-for-sustainable-development-technologies.\n\n\nRashid, Layali, Karthik Pattabiraman, and Sathish Gopalakrishnan. 2012.\n“Intermittent Hardware Errors Recovery: Modeling and\nEvaluation.” In 2012 Ninth International Conference on\nQuantitative Evaluation of Systems, 220–29. IEEE; IEEE. https://doi.org/10.1109/qest.2012.37.\n\n\n———. 2015. “Characterizing the Impact of Intermittent Hardware\nFaults on Programs.” IEEE Trans. Reliab. 64 (1):\n297–310. https://doi.org/10.1109/tr.2014.2363152.\n\n\nRatner, Alex, Braden Hancock, Jared Dunnmon, Roger Goldman, and\nChristopher Ré. 2018. “Snorkel MeTaL: Weak\nSupervision for Multi-Task Learning.” In Proceedings of the\nSecond Workshop on Data Management for End-to-End Machine Learning.\nACM. https://doi.org/10.1145/3209889.3209898.\n\n\nReagen, Brandon, Udit Gupta, Lillian Pentecost, Paul Whatmough, Sae Kyu\nLee, Niamh Mulholland, David Brooks, and Gu-Yeon Wei. 2018. “Ares:\nA Framework for Quantifying the Resilience of Deep Neural\nNetworks.” In 2018 55th ACM/ESDA/IEEE Design Automation\nConference (DAC), 1–6. IEEE. https://doi.org/10.1109/dac.2018.8465834.\n\n\nReagen, Brandon, Jose Miguel Hernandez-Lobato, Robert Adolf, Michael\nGelbart, Paul Whatmough, Gu-Yeon Wei, and David Brooks. 2017. “A\nCase for Efficient Accelerator Design Space Exploration via\nBayesian Optimization.” In 2017 IEEE/ACM\nInternational Symposium on Low Power Electronics and Design\n(ISLPED), 1–6. IEEE; IEEE. https://doi.org/10.1109/islped.2017.8009208.\n\n\nReddi, Sashank J., Satyen Kale, and Sanjiv Kumar. 2019. “On the\nConvergence of Adam and Beyond.” arXiv Preprint\narXiv:1904.09237, April. http://arxiv.org/abs/1904.09237v1.\n\n\nReddi, Vijay Janapa, Christine Cheng, David Kanter, Peter Mattson,\nGuenther Schmuelling, Carole-Jean Wu, Brian Anderson, et al. 2020.\n“MLPerf Inference Benchmark.” In 2020\nACM/IEEE 47th Annual International Symposium on Computer Architecture\n(ISCA), 446–59. IEEE; IEEE. https://doi.org/10.1109/isca45697.2020.00045.\n\n\nReddi, Vijay Janapa, and Meeta Sharma Gupta. 2013. Resilient\nArchitecture Design for Voltage Variation. Springer International\nPublishing. https://doi.org/10.1007/978-3-031-01739-1.\n\n\nReis, G. A., J. Chang, N. Vachharajani, R. Rangan, and D. I. August.\n2005. “SWIFT: Software Implemented Fault\nTolerance.” In International Symposium on Code Generation and\nOptimization, 243–54. IEEE; IEEE. https://doi.org/10.1109/cgo.2005.34.\n\n\nRibeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. 2016.\n“” Why Should i Trust You?” Explaining\nthe Predictions of Any Classifier.” In Proceedings of the\n22nd ACM SIGKDD International Conference on Knowledge Discovery and Data\nMining, 1135–44.\n\n\nRobbins, Herbert, and Sutton Monro. 1951. “A Stochastic\nApproximation Method.” The Annals of Mathematical\nStatistics 22 (3): 400–407. https://doi.org/10.1214/aoms/1177729586.\n\n\nRombach, Robin, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and\nBjorn Ommer. 2022. “High-Resolution Image Synthesis with Latent\nDiffusion Models.” In 2022 IEEE/CVF Conference on Computer\nVision and Pattern Recognition (CVPR). IEEE. https://doi.org/10.1109/cvpr52688.2022.01042.\n\n\nRosa, Gustavo H. de, and João P. Papa. 2021. “A Survey on Text\nGeneration Using Generative Adversarial Networks.” Pattern\nRecogn. 119 (November): 108098. https://doi.org/10.1016/j.patcog.2021.108098.\n\n\nRosenblatt, Frank. 1957. The Perceptron, a Perceiving and\nRecognizing Automaton Project Para. Cornell Aeronautical\nLaboratory.\n\n\nRoskies, Adina. 2002. “Neuroethics for the New Millenium.”\nNeuron 35 (1): 21–23. https://doi.org/10.1016/s0896-6273(02)00763-8.\n\n\nRuder, Sebastian. 2016. “An Overview of Gradient Descent\nOptimization Algorithms.” ArXiv Preprint abs/1609.04747\n(September). http://arxiv.org/abs/1609.04747v2.\n\n\nRudin, Cynthia. 2019. “Stop Explaining Black Box Machine Learning\nModels for High Stakes Decisions and Use Interpretable Models\nInstead.” Nature Machine Intelligence 1 (5): 206–15. https://doi.org/10.1038/s42256-019-0048-x.\n\n\nRumelhart, David E., Geoffrey E. Hinton, and Ronald J. Williams. 1986.\n“Learning Representations by Back-Propagating Errors.”\nNature 323 (6088): 533–36. https://doi.org/10.1038/323533a0.\n\n\nRussakovsky, Olga, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh,\nSean Ma, Zhiheng Huang, et al. 2015. “ImageNet Large\nScale Visual Recognition Challenge.” Int. J. Comput.\nVision 115 (3): 211–52. https://doi.org/10.1007/s11263-015-0816-y.\n\n\nRussell, Stuart. 2021. “Human-Compatible Artificial\nIntelligence.” Human-Like Machine Intelligence, 3–23.\n\n\nRyan, Richard M., and Edward L. Deci. 2000. “Self-Determination\nTheory and the Facilitation of Intrinsic Motivation, Social Development,\nand Well-Being.” Am. Psychol. 55 (1): 68–78. https://doi.org/10.1037/0003-066x.55.1.68.\n\n\nSamajdar, Ananda, Yuhao Zhu, Paul Whatmough, Matthew Mattina, and Tushar\nKrishna. 2018. “Scale-Sim: Systolic Cnn Accelerator\nSimulator.” ArXiv Preprint abs/1811.02883. https://arxiv.org/abs/1811.02883.\n\n\nSambasivan, Nithya, Shivani Kapania, Hannah Highfill, Diana Akrong,\nPraveen Paritosh, and Lora M Aroyo. 2021a.\n““Everyone Wants to Do the Model Work,\nNot the Data Work”: Data Cascades in\nHigh-Stakes AI.” In Proceedings of the 2021 CHI\nConference on Human Factors in Computing Systems, 1–15.\n\n\n———. 2021b. ““Everyone Wants to Do the\nModel Work, Not the Data Work”: Data Cascades\nin High-Stakes AI.” In Proceedings of the 2021\nCHI Conference on Human Factors in Computing Systems. CHI ’21. New\nYork, NY, USA: ACM. https://doi.org/10.1145/3411764.3445518.\n\n\n———. 2021c. ““Everyone Wants to Do the\nModel Work, Not the Data Work”: Data Cascades\nin High-Stakes AI.” In Proceedings of the 2021\nCHI Conference on Human Factors in Computing Systems. ACM. https://doi.org/10.1145/3411764.3445518.\n\n\nSangchoolie, Behrooz, Karthik Pattabiraman, and Johan Karlsson. 2017.\n“One Bit Is (Not) Enough: An Empirical\nStudy of the Impact of Single and Multiple Bit-Flip Errors.” In\n2017 47th Annual IEEE/IFIP International Conference on Dependable\nSystems and Networks (DSN), 97–108. IEEE; IEEE. https://doi.org/10.1109/dsn.2017.30.\n\n\nSchäfer, Mike S. 2023. “The Notorious GPT:\nScience Communication in the Age of Artificial\nIntelligence.” Journal of Science Communication 22 (02):\nY02. https://doi.org/10.22323/2.22020402.\n\n\nSchizas, Nikolaos, Aristeidis Karras, Christos Karras, and Spyros\nSioutas. 2022. “TinyML for Ultra-Low Power\nAI and Large Scale IoT Deployments:\nA Systematic Review.” Future Internet 14\n(12): 363. https://doi.org/10.3390/fi14120363.\n\n\nSchuman, Catherine D., Shruti R. Kulkarni, Maryam Parsa, J. Parker\nMitchell, Prasanna Date, and Bill Kay. 2022. “Opportunities for\nNeuromorphic Computing Algorithms and Applications.” Nature\nComputational Science 2 (1): 10–19. https://doi.org/10.1038/s43588-021-00184-y.\n\n\nSchwartz, Daniel, Jonathan Michael Gomes Selman, Peter Wrege, and\nAndreas Paepcke. 2021. “Deployment of Embedded\nEdge-AI for Wildlife Monitoring in Remote Regions.”\nIn 2021 20th IEEE International Conference on Machine Learning and\nApplications (ICMLA), 1035–42. IEEE; IEEE. https://doi.org/10.1109/icmla52953.2021.00170.\n\n\nSchwartz, Roy, Jesse Dodge, Noah A. Smith, and Oren Etzioni. 2020.\n“Green AI.” Commun. ACM 63 (12):\n54–63. https://doi.org/10.1145/3381831.\n\n\nSegal, Mark, and Kurt Akeley. 1999. “The OpenGL\nGraphics System: A Specification (Version 1.1).”\n\n\nSegura Anaya, L. H., Abeer Alsadoon, N. Costadopoulos, and P. W. C.\nPrasad. 2017. “Ethical Implications of User Perceptions of\nWearable Devices.” Sci. Eng. Ethics 24 (1): 1–28. https://doi.org/10.1007/s11948-017-9872-8.\n\n\nSeide, Frank, and Amit Agarwal. 2016. “Cntk: Microsoft’s\nOpen-Source Deep-Learning Toolkit.” In Proceedings of the\n22nd ACM SIGKDD International Conference on Knowledge Discovery and Data\nMining, 2135–35. ACM. https://doi.org/10.1145/2939672.2945397.\n\n\nSelvaraju, Ramprasaath R., Michael Cogswell, Abhishek Das, Ramakrishna\nVedantam, Devi Parikh, and Dhruv Batra. 2017.\n“Grad-CAM: Visual Explanations from Deep\nNetworks via Gradient-Based Localization.” In 2017 IEEE\nInternational Conference on Computer Vision (ICCV), 618–26. IEEE.\nhttps://doi.org/10.1109/iccv.2017.74.\n\n\nSeong, Nak Hee, Dong Hyuk Woo, Vijayalakshmi Srinivasan, Jude A. Rivers,\nand Hsien-Hsin S. Lee. 2010. “SAFER: Stuck-at-fault Error Recovery for\nMemories.” In 2010 43rd Annual IEEE/ACM International\nSymposium on Microarchitecture, 115–24. IEEE; IEEE. https://doi.org/10.1109/micro.2010.46.\n\n\nSeyedzadeh, Saleh, Farzad Pour Rahimian, Ivan Glesk, and Marc Roper.\n2018. “Machine Learning for Estimation of Building Energy\nConsumption and Performance: A Review.”\nVisualization in Engineering 6 (1): 1–20. https://doi.org/10.1186/s40327-018-0064-7.\n\n\nShalev-Shwartz, Shai, Shaked Shammah, and Amnon Shashua. 2017. “On\na Formal Model of Safe and Scalable Self-Driving Cars.” ArXiv\nPreprint abs/1708.06374. https://arxiv.org/abs/1708.06374.\n\n\nShan, Shawn, Wenxin Ding, Josephine Passananti, Haitao Zheng, and Ben Y\nZhao. 2023. “Prompt-Specific Poisoning Attacks on Text-to-Image\nGenerative Models.” ArXiv Preprint abs/2310.13828. https://arxiv.org/abs/2310.13828.\n\n\nShastri, Bhavin J., Alexander N. Tait, T. Ferreira de Lima, Wolfram H.\nP. Pernice, Harish Bhaskaran, C. D. Wright, and Paul R. Prucnal. 2021.\n“Photonics for Artificial Intelligence and Neuromorphic\nComputing.” Nat. Photonics 15 (2): 102–14. https://doi.org/10.1038/s41566-020-00754-y.\n\n\nSheaffer, Jeremy W, David P Luebke, and Kevin Skadron. 2007. “A\nHardware Redundancy and Recovery Mechanism for Reliable Scientific\nComputation on Graphics Processors.” In Graphics\nHardware, 2007:55–64. Citeseer.\n\n\nShehabi, Arman, Sarah Smith, Dale Sartor, Richard Brown, Magnus Herrlin,\nJonathan Koomey, Eric Masanet, Nathaniel Horner, Inês Azevedo, and\nWilliam Lintner. 2016. “United States Data Center Energy Usage\nReport.”\n\n\nShen, Sheng, Zhen Dong, Jiayu Ye, Linjian Ma, Zhewei Yao, Amir Gholami,\nMichael W. Mahoney, and Kurt Keutzer. 2020. “Q-BERT:\nHessian Based Ultra Low Precision Quantization of\nBERT.” Proceedings of the AAAI Conference on\nArtificial Intelligence 34 (05): 8815–21. https://doi.org/10.1609/aaai.v34i05.6409.\n\n\nSheng, Victor S., and Jing Zhang. 2019. “Machine Learning with\nCrowdsourcing: A Brief Summary of the Past Research and\nFuture Directions.” Proceedings of the AAAI Conference on\nArtificial Intelligence 33 (01): 9837–43. https://doi.org/10.1609/aaai.v33i01.33019837.\n\n\nShi, Hongrui, and Valentin Radu. 2022. “Data Selection for\nEfficient Model Update in Federated Learning.” In Proceedings\nof the 2nd European Workshop on Machine Learning and Systems,\n72–78. ACM. https://doi.org/10.1145/3517207.3526980.\n\n\nShneiderman, Ben. 2020. “Bridging the Gap Between Ethics and\nPractice: Guidelines for Reliable, Safe, and Trustworthy Human-Centered\nAI Systems.” ACM Trans. Interact. Intell. Syst. 10 (4):\n1–31. https://doi.org/10.1145/3419764.\n\n\n———. 2022. Human-Centered AI. Oxford University\nPress.\n\n\nShokri, Reza, Marco Stronati, Congzheng Song, and Vitaly Shmatikov.\n2017. “Membership Inference Attacks Against Machine Learning\nModels.” In 2017 IEEE Symposium on Security and Privacy\n(SP), 3–18. IEEE; IEEE. https://doi.org/10.1109/sp.2017.41.\n\n\nSiddik, Md Abu Bakar, Arman Shehabi, and Landon Marston. 2021.\n“The Environmental Footprint of Data Centers in the United\nStates.” Environ. Res. Lett. 16 (6): 064017. https://doi.org/10.1088/1748-9326/abfba1.\n\n\nSilvestro, Daniele, Stefano Goria, Thomas Sterner, and Alexandre\nAntonelli. 2022. “Improving Biodiversity Protection Through\nArtificial Intelligence.” Nature Sustainability 5 (5):\n415–24. https://doi.org/10.1038/s41893-022-00851-6.\n\n\nSingh, Narendra, and Oladele A. Ogunseitan. 2022. “Disentangling\nthe Worldwide Web of e-Waste and Climate Change Co-Benefits.”\nCircular Economy 1 (2): 100011. https://doi.org/10.1016/j.cec.2022.100011.\n\n\nSkorobogatov, Sergei. 2009. “Local Heating Attacks on Flash Memory\nDevices.” In 2009 IEEE International Workshop on\nHardware-Oriented Security and Trust, 1–6. IEEE; IEEE. https://doi.org/10.1109/hst.2009.5225028.\n\n\nSkorobogatov, Sergei P, and Ross J Anderson. 2003. “Optical Fault\nInduction Attacks.” In Cryptographic Hardware and Embedded\nSystems-CHES 2002: 4th International Workshop Redwood Shores, CA, USA,\nAugust 1315, 2002 Revised Papers 4, 2–12. Springer.\n\n\nSmilkov, Daniel, Nikhil Thorat, Been Kim, Fernanda Viégas, and Martin\nWattenberg. 2017. “Smoothgrad: Removing Noise by\nAdding Noise.” ArXiv Preprint abs/1706.03825. https://arxiv.org/abs/1706.03825.\n\n\nSnoek, Jasper, Hugo Larochelle, and Ryan P. Adams. 2012.\n“Practical Bayesian Optimization of Machine Learning\nAlgorithms.” In Advances in Neural Information Processing\nSystems 25: 26th Annual Conference on Neural Information Processing\nSystems 2012. Proceedings of a Meeting Held December 3-6, 2012, Lake\nTahoe, Nevada, United States, edited by Peter L. Bartlett, Fernando\nC. N. Pereira, Christopher J. C. Burges, Léon Bottou, and Kilian Q.\nWeinberger, 2960–68. https://proceedings.neurips.cc/paper/2012/hash/05311655a15b75fab86956663e1819cd-Abstract.html.\n\n\nSrivastava, Nitish, Geoffrey E. Hinton, Alex Krizhevsky, Ilya Sutskever,\nand Ruslan Salakhutdinov. 2014. “Dropout: A Simple Way to Prevent\nNeural Networks from Overfitting.” J. Mach. Learn. Res.\n15 (1): 1929–58. https://doi.org/10.5555/2627435.2670313.\n\n\nStm32L4Q5Ag. 2021. STMicroelectronics.\n\n\nStrubell, Emma, Ananya Ganesh, and Andrew McCallum. 2019. “Energy\nand Policy Considerations for Deep Learning in NLP.”\nIn Proceedings of the 57th Annual Meeting of the Association for\nComputational Linguistics, 3645–50. Florence, Italy: Association\nfor Computational Linguistics. https://doi.org/10.18653/v1/p19-1355.\n\n\nSuda, Naveen, Vikas Chandra, Ganesh Dasika, Abinash Mohanty, Yufei Ma,\nSarma Vrudhula, Jae-sun Seo, and Yu Cao. 2016.\n“Throughput-Optimized OpenCL-Based FPGA\nAccelerator for Large-Scale Convolutional Neural Networks.” In\nProceedings of the 2016 ACM/SIGDA International Symposium on\nField-Programmable Gate Arrays, 16–25. ACM. https://doi.org/10.1145/2847263.2847276.\n\n\nSudhakar, Soumya, Vivienne Sze, and Sertac Karaman. 2023. “Data\nCenters on Wheels: Emissions from Computing Onboard\nAutonomous Vehicles.” IEEE Micro 43 (1): 29–39. https://doi.org/10.1109/mm.2022.3219803.\n\n\nSze, Vivienne, Yu-Hsin Chen, Tien-Ju Yang, and Joel S. Emer. 2017.\n“Efficient Processing of Deep Neural Networks: A\nTutorial and Survey.” Proc. IEEE 105 (12): 2295–2329. https://doi.org/10.1109/jproc.2017.2761740.\n\n\nSzegedy, Christian, Wojciech Zaremba, Ilya Sutskever, Joan Bruna,\nDumitru Erhan, Ian J. Goodfellow, and Rob Fergus. 2014.\n“Intriguing Properties of Neural Networks.” In 2nd\nInternational Conference on Learning Representations, ICLR 2014, Banff,\nAB, Canada, April 14-16, 2014, Conference Track Proceedings, edited\nby Yoshua Bengio and Yann LeCun. http://arxiv.org/abs/1312.6199.\n\n\nTambe, Thierry, En-Yu Yang, Zishen Wan, Yuntian Deng, Vijay Janapa\nReddi, Alexander Rush, David Brooks, and Gu-Yeon Wei. 2020.\n“Algorithm-Hardware Co-Design of Adaptive Floating-Point Encodings\nfor Resilient Deep Learning Inference.” In 2020 57th ACM/IEEE\nDesign Automation Conference (DAC), 1–6. IEEE; IEEE. https://doi.org/10.1109/dac18072.2020.9218516.\n\n\nTan, Mingxing, Bo Chen, Ruoming Pang, Vijay Vasudevan, Mark Sandler,\nAndrew Howard, and Quoc V. Le. 2019. “MnasNet: Platform-aware Neural Architecture Search for\nMobile.” In 2019 IEEE/CVF Conference on Computer Vision and\nPattern Recognition (CVPR), 2820–28. IEEE. https://doi.org/10.1109/cvpr.2019.00293.\n\n\nTan, Mingxing, and Quoc V. Le. 2023. “Demystifying Deep\nLearning.” Wiley. https://doi.org/10.1002/9781394205639.ch6.\n\n\nTang, Xin, Yichun He, and Jia Liu. 2022. “Soft Bioelectronics for\nCardiac Interfaces.” Biophysics Reviews 3 (1). https://doi.org/10.1063/5.0069516.\n\n\nTang, Xin, Hao Shen, Siyuan Zhao, Na Li, and Jia Liu. 2023.\n“Flexible Braincomputer Interfaces.”\nNature Electronics 6 (2): 109–18. https://doi.org/10.1038/s41928-022-00913-9.\n\n\nTarun, Ayush K, Vikram S Chundawat, Murari Mandal, and Mohan\nKankanhalli. 2022. “Deep Regression Unlearning.” ArXiv\nPreprint abs/2210.08196. https://arxiv.org/abs/2210.08196.\n\n\nTeam, The Theano Development, Rami Al-Rfou, Guillaume Alain, Amjad\nAlmahairi, Christof Angermueller, Dzmitry Bahdanau, Nicolas Ballas, et\nal. 2016. “Theano: A Python Framework for Fast\nComputation of Mathematical Expressions.” https://arxiv.org/abs/1605.02688.\n\n\n“The Ultimate Guide to Deep Learning Model Quantization and\nQuantization-Aware Training.” n.d. https://deci.ai/quantization-and-quantization-aware-training/.\n\n\nThompson, Neil C., Kristjan Greenewald, Keeheon Lee, and Gabriel F.\nManso. 2021. “Deep Learning’s Diminishing Returns:\nThe Cost of Improvement Is Becoming Unsustainable.”\nIEEE Spectr. 58 (10): 50–55. https://doi.org/10.1109/mspec.2021.9563954.\n\n\nTill, Aaron, Andrew L. Rypel, Andrew Bray, and Samuel B. Fey. 2019.\n“Fish Die-Offs Are Concurrent with Thermal Extremes in North\nTemperate Lakes.” Nat. Clim. Change 9 (8): 637–41. https://doi.org/10.1038/s41558-019-0520-y.\n\n\nTirtalistyani, Rose, Murtiningrum Murtiningrum, and Rameshwar S. Kanwar.\n2022. “Indonesia Rice Irrigation System:\nTime for Innovation.” Sustainability 14\n(19): 12477. https://doi.org/10.3390/su141912477.\n\n\nTokui, Seiya, Ryosuke Okuta, Takuya Akiba, Yusuke Niitani, Toru Ogawa,\nShunta Saito, Shuji Suzuki, Kota Uenishi, Brian Vogel, and Hiroyuki\nYamazaki Vincent. 2019. “Chainer: A Deep Learning Framework for\nAccelerating the Research Cycle.” In Proceedings of the 25th\nACM SIGKDD International Conference on Knowledge Discovery &Amp;\nData Mining, 5:1–6. ACM. https://doi.org/10.1145/3292500.3330756.\n\n\nTramèr, Florian, Pascal Dupré, Gili Rusak, Giancarlo Pellegrino, and Dan\nBoneh. 2019. “AdVersarial: Perceptual Ad Blocking\nMeets Adversarial Machine Learning.” In Proceedings of the\n2019 ACM SIGSAC Conference on Computer and Communications Security,\n2005–21. ACM. https://doi.org/10.1145/3319535.3354222.\n\n\nTran, Cuong, Ferdinando Fioretto, Jung-Eun Kim, and Rakshit Naidu. 2022.\n“Pruning Has a Disparate Impact on Model Accuracy.” Adv\nNeural Inf Process Syst 35: 17652–64.\n\n\nTsai, Min-Jen, Ping-Yi Lin, and Ming-En Lee. 2023. “Adversarial\nAttacks on Medical Image Classification.” Cancers 15\n(17): 4228. https://doi.org/10.3390/cancers15174228.\n\n\nTsai, Timothy, Siva Kumar Sastry Hari, Michael Sullivan, Oreste Villa,\nand Stephen W. Keckler. 2021. “NVBitFI:\nDynamic Fault Injection for GPUs.” In\n2021 51st Annual IEEE/IFIP International Conference on Dependable\nSystems and Networks (DSN), 284–91. IEEE; IEEE. https://doi.org/10.1109/dsn48987.2021.00041.\n\n\nUddin, Mueen, and Azizah Abdul Rahman. 2012. “Energy Efficiency\nand Low Carbon Enabler Green IT Framework for Data Centers\nConsidering Green Metrics.” Renewable Sustainable Energy\nRev. 16 (6): 4078–94. https://doi.org/10.1016/j.rser.2012.03.014.\n\n\nUn, and World Economic Forum. 2019. A New Circular Vision for\nElectronics, Time for a Global Reboot. PACE - Platform for\nAccelerating the Circular Economy. https://www3.weforum.org/docs/WEF\\_A\\_New\\_Circular\\_Vision\\_for\\_Electronics.pdf.\n\n\nValenzuela, Christine L, and Pearl Y Wang. 2000. “A Genetic\nAlgorithm for VLSI Floorplanning.” In Parallel\nProblem Solving from Nature PPSN VI: 6th International Conference Paris,\nFrance, September 1820, 2000 Proceedings 6, 671–80.\nSpringer.\n\n\nVan Noorden, Richard. 2016. “ArXiv Preprint Server\nPlans Multimillion-Dollar Overhaul.” Nature 534 (7609):\n602–2. https://doi.org/10.1038/534602a.\n\n\nVangal, Sriram, Somnath Paul, Steven Hsu, Amit Agarwal, Saurabh Kumar,\nRam Krishnamurthy, Harish Krishnamurthy, James Tschanz, Vivek De, and\nChris H. Kim. 2021. “Wide-Range Many-Core SoC Design\nin Scaled CMOS: Challenges and\nOpportunities.” IEEE Trans. Very Large Scale Integr. VLSI\nSyst. 29 (5): 843–56. https://doi.org/10.1109/tvlsi.2021.3061649.\n\n\nVaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion\nJones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017.\n“Attention Is All You Need.” Adv Neural Inf Process\nSyst 30.\n\n\n“Vector-Borne Diseases.” n.d.\nhttps://www.who.int/news-room/fact-sheets/detail/vector-borne-diseases.\n\n\nVelazco, Raoul, Gilles Foucard, and Paul Peronnard. 2010.\n“Combining Results of Accelerated Radiation Tests and Fault\nInjections to Predict the Error Rate of an Application Implemented in\nSRAM-Based FPGAs.” IEEE Trans.\nNucl. Sci. 57 (6): 3500–3505. https://doi.org/10.1109/tns.2010.2087355.\n\n\nVerma, Naveen, Hongyang Jia, Hossein Valavi, Yinqi Tang, Murat Ozatay,\nLung-Yen Chen, Bonan Zhang, and Peter Deaville. 2019. “In-Memory\nComputing: Advances and Prospects.” IEEE\nSolid-State Circuits Mag. 11 (3): 43–55. https://doi.org/10.1109/mssc.2019.2922889.\n\n\nVerma, Team Dual_Boot: Swapnil. 2022. “Elephant\nAI.” Hackster.io. https://www.hackster.io/dual\\_boot/elephant-ai-ba71e9.\n\n\nVinuesa, Ricardo, Hossein Azizpour, Iolanda Leite, Madeline Balaam,\nVirginia Dignum, Sami Domisch, Anna Felländer, Simone Daniela Langhans,\nMax Tegmark, and Francesco Fuso Nerini. 2020. “The Role of\nArtificial Intelligence in Achieving the Sustainable Development\nGoals.” Nat. Commun. 11 (1): 1–10. https://doi.org/10.1038/s41467-019-14108-y.\n\n\nVivet, Pascal, Eric Guthmuller, Yvain Thonnart, Gael Pillonnet, Cesar\nFuguet, Ivan Miro-Panades, Guillaume Moritz, et al. 2021.\n“IntAct: A 96-Core Processor with Six\nChiplets 3D-Stacked on an Active Interposer with\nDistributed Interconnects and Integrated Power Management.”\nIEEE J. Solid-State Circuits 56 (1): 79–97. https://doi.org/10.1109/jssc.2020.3036341.\n\n\nWachter, Sandra, Brent Mittelstadt, and Chris Russell. 2017.\n“Counterfactual Explanations Without Opening the Black Box:\nAutomated Decisions and the GDPR.”\nSSRN Electronic Journal 31: 841. https://doi.org/10.2139/ssrn.3063289.\n\n\nWald, Peter H., and Jeffrey R. Jones. 1987. “Semiconductor\nManufacturing: An Introduction to Processes and\nHazards.” Am. J. Ind. Med. 11 (2): 203–21. https://doi.org/10.1002/ajim.4700110209.\n\n\nWan, Zishen, Aqeel Anwar, Yu-Shun Hsiao, Tianyu Jia, Vijay Janapa Reddi,\nand Arijit Raychowdhury. 2021. “Analyzing and Improving Fault\nTolerance of Learning-Based Navigation Systems.” In 2021 58th\nACM/IEEE Design Automation Conference (DAC), 841–46. IEEE; IEEE. https://doi.org/10.1109/dac18074.2021.9586116.\n\n\nWan, Zishen, Yiming Gan, Bo Yu, S Liu, A Raychowdhury, and Y Zhu. 2023.\n“Vpp: The Vulnerability-Proportional Protection\nParadigm Towards Reliable Autonomous Machines.” In\nProceedings of the 5th International Workshop on Domain Specific\nSystem Architecture (DOSSA), 1–6.\n\n\nWang, LingFeng, and YaQing Zhan. 2019a. “A Conceptual Peer Review\nModel for arXiv and Other Preprint\nDatabases.” Learn. Publ. 32 (3): 213–19. https://doi.org/10.1002/leap.1229.\n\n\n———. 2019b. “A Conceptual Peer Review Model for arXiv and Other Preprint Databases.”\nLearn. Publ. 32 (3): 213–19. https://doi.org/10.1002/leap.1229.\n\n\nWang, Tianzhe, Kuan Wang, Han Cai, Ji Lin, Zhijian Liu, Hanrui Wang,\nYujun Lin, and Song Han. 2020. “APQ:\nJoint Search for Network Architecture, Pruning and\nQuantization Policy.” In 2020 IEEE/CVF Conference on Computer\nVision and Pattern Recognition (CVPR), 2075–84. IEEE. https://doi.org/10.1109/cvpr42600.2020.00215.\n\n\nWarden, Pete. 2018. “Speech Commands: A Dataset for\nLimited-Vocabulary Speech Recognition.” arXiv Preprint\narXiv:1804.03209.\n\n\nWarden, Pete, and Daniel Situnayake. 2019. Tinyml:\nMachine Learning with Tensorflow Lite on Arduino and\nUltra-Low-Power Microcontrollers. O’Reilly Media.\n\n\nWeik, Martin H. 1955. A Survey of Domestic Electronic Digital\nComputing Systems. Ballistic Research Laboratories.\n\n\nWeiser, Mark. 1991. “The Computer for the 21st Century.”\nSci. Am. 265 (3): 94–104. https://doi.org/10.1038/scientificamerican0991-94.\n\n\nWess, Matthias, Matvey Ivanov, Christoph Unger, and Anvesh Nookala.\n2020. “ANNETTE: Accurate Neural Network\nExecution Time Estimation with Stacked Models.” IEEE. https://doi.org/10.1109/ACCESS.2020.3047259.\n\n\nWiener, Norbert. 1960. “Some Moral and Technical Consequences of\nAutomation: As Machines Learn They May Develop Unforeseen Strategies at\nRates That Baffle Their Programmers.” Science 131\n(3410): 1355–58. https://doi.org/10.1126/science.131.3410.1355.\n\n\nWilkening, Mark, Vilas Sridharan, Si Li, Fritz Previlon, Sudhanva\nGurumurthi, and David R. Kaeli. 2014. “Calculating Architectural\nVulnerability Factors for Spatial Multi-Bit Transient Faults.” In\n2014 47th Annual IEEE/ACM International Symposium on\nMicroarchitecture, 293–305. IEEE; IEEE. https://doi.org/10.1109/micro.2014.15.\n\n\nWinkler, Harald, Franck Lecocq, Hans Lofgren, Maria Virginia Vilariño,\nSivan Kartha, and Joana Portugal-Pereira. 2022. “Examples of\nShifting Development Pathways: Lessons on How to Enable\nBroader, Deeper, and Faster Climate Action.” Climate\nAction 1 (1). https://doi.org/10.1007/s44168-022-00026-1.\n\n\nWong, H.-S. Philip, Heng-Yuan Lee, Shimeng Yu, Yu-Sheng Chen, Yi Wu,\nPang-Shiu Chen, Byoungil Lee, Frederick T. Chen, and Ming-Jinn Tsai.\n2012. “MetalOxide\nRRAM.” Proc. IEEE 100 (6): 1951–70. https://doi.org/10.1109/jproc.2012.2190369.\n\n\nWu, Bichen, Kurt Keutzer, Xiaoliang Dai, Peizhao Zhang, Yanghan Wang,\nFei Sun, Yiming Wu, Yuandong Tian, Peter Vajda, and Yangqing Jia. 2019.\n“FBNet: Hardware-aware\nEfficient ConvNet Design via Differentiable Neural\nArchitecture Search.” In 2019 IEEE/CVF Conference on Computer\nVision and Pattern Recognition (CVPR), 10734–42. IEEE. https://doi.org/10.1109/cvpr.2019.01099.\n\n\nWu, Carole-Jean, Ramya Raghavendra, Udit Gupta, Bilge Acun, Newsha\nArdalani, Kiwan Maeng, Gloria Chang, et al. 2022. “Sustainable Ai:\nEnvironmental Implications, Challenges and\nOpportunities.” Proceedings of Machine Learning and\nSystems 4: 795–813.\n\n\nWu, Zhang Judd, and Micikevicius Isaev. 2020. “Integer\nQuantization for Deep Learning Inference: Principles and\nEmpirical Evaluation).” ArXiv Preprint. https://arxiv.org/abs/2004.09602.\n\n\nXiao, Seznec Lin, Demouth Wu, and Han. 2022.\n“SmoothQuant: Accurate and Efficient\nPost-Training Quantization for Large Language Models.” ArXiv\nPreprint. https://arxiv.org/abs/2211.10438.\n\n\nXie, Cihang, Mingxing Tan, Boqing Gong, Jiang Wang, Alan L. Yuille, and\nQuoc V. Le. 2020. “Adversarial Examples Improve Image\nRecognition.” In 2020 IEEE/CVF Conference on Computer Vision\nand Pattern Recognition (CVPR), 816–25. IEEE. https://doi.org/10.1109/cvpr42600.2020.00090.\n\n\nXie, Saining, Ross Girshick, Piotr Dollar, Zhuowen Tu, and Kaiming He.\n2017. “Aggregated Residual Transformations for Deep Neural\nNetworks.” In 2017 IEEE Conference on Computer Vision and\nPattern Recognition (CVPR), 1492–1500. IEEE. https://doi.org/10.1109/cvpr.2017.634.\n\n\nXinyu, Chen. n.d.\n\n\nXiong, Siyu, Guoqing Wu, Xitian Fan, Xuan Feng, Zhongcheng Huang, Wei\nCao, Xuegong Zhou, et al. 2021. “MRI-Based Brain\nTumor Segmentation Using FPGA-Accelerated Neural\nNetwork.” BMC Bioinf. 22 (1): 421. https://doi.org/10.1186/s12859-021-04347-6.\n\n\nXiu, Liming. 2019. “Time Moore: Exploiting Moore’s Law from the Perspective of Time.”\nIEEE Solid-State Circuits Mag. 11 (1): 39–55. https://doi.org/10.1109/mssc.2018.2882285.\n\n\nXu, Chen, Jianqiang Yao, Zhouchen Lin, Wenwu Ou, Yuanbin Cao, Zhirong\nWang, and Hongbin Zha. 2018. “Alternating Multi-Bit Quantization\nfor Recurrent Neural Networks.” In 6th International\nConference on Learning Representations, ICLR 2018, Vancouver, BC,\nCanada, April 30 - May 3, 2018, Conference Track Proceedings.\nOpenReview.net. https://openreview.net/forum?id=S19dR9x0b.\n\n\nXu, Hu, Saining Xie, Xiaoqing Ellen Tan, Po-Yao Huang, Russell Howes,\nVasu Sharma, Shang-Wen Li, Gargi Ghosh, Luke Zettlemoyer, and Christoph\nFeichtenhofer. 2023. “Demystifying CLIP Data.”\nArXiv Preprint abs/2309.16671. https://arxiv.org/abs/2309.16671.\n\n\nXu, Ying, Xu Zhong, Antonio Jimeno Yepes, and Jey Han Lau. 2021.\n“Grey-Box Adversarial Attack and Defence for\nSentiment Classification.” arXiv Preprint\narXiv:2103.11576.\n\n\nXu, Zheng, Yanxiang Zhang, Galen Andrew, Christopher A Choquette-Choo,\nPeter Kairouz, H Brendan McMahan, Jesse Rosenstock, and Yuanbo Zhang.\n2023. “Federated Learning of Gboard Language Models with\nDifferential Privacy.” ArXiv Preprint abs/2305.18465. https://arxiv.org/abs/2305.18465.\n\n\nYang, Tien-Ju, Yonghui Xiao, Giovanni Motta, Françoise Beaufays, Rajiv\nMathews, and Mingqing Chen. 2023. “Online Model Compression for\nFederated Learning with Large Models.” In ICASSP 2023 - 2023\nIEEE International Conference on Acoustics, Speech and Signal Processing\n(ICASSP), 1–5. IEEE; IEEE. https://doi.org/10.1109/icassp49357.2023.10097124.\n\n\nYao, Zhewei, Zhen Dong, Zhangcheng Zheng, Amir Gholami, Jiali Yu, Eric\nTan, Leyuan Wang, et al. 2021. “Hawq-V3: Dyadic\nNeural Network Quantization.” In International Conference on\nMachine Learning, 11875–86. PMLR.\n\n\nYe, Linfeng, and Shayan Mohajer Hamidi. 2021. “Thundernna:\nA White Box Adversarial Attack.” arXiv Preprint\narXiv:2111.12305.\n\n\nYeh, Y. C. 1996. “Triple-Triple Redundant 777 Primary Flight\nComputer.” In 1996 IEEE Aerospace Applications Conference.\nProceedings, 1:293–307. IEEE; IEEE. https://doi.org/10.1109/aero.1996.495891.\n\n\nYik, Jason, Soikat Hasan Ahmed, Zergham Ahmed, Brian Anderson, Andreas\nG. Andreou, Chiara Bartolozzi, Arindam Basu, et al. 2023.\n“NeuroBench: Advancing Neuromorphic\nComputing Through Collaborative, Fair and Representative\nBenchmarking.” https://arxiv.org/abs/2304.04640.\n\n\nYou, Jie, Jae-Won Chung, and Mosharaf Chowdhury. 2023. “Zeus:\nUnderstanding and Optimizing GPU Energy\nConsumption of DNN Training.” In 20th USENIX\nSymposium on Networked Systems Design and Implementation (NSDI 23),\n119–39. Boston, MA: USENIX Association. https://www.usenix.org/conference/nsdi23/presentation/you.\n\n\nYou, Yang, Zhao Zhang, Cho-Jui Hsieh, James Demmel, and Kurt Keutzer.\n2017. “ImageNet Training in Minutes,” September. http://arxiv.org/abs/1709.05011v10.\n\n\nYoung, Tom, Devamanyu Hazarika, Soujanya Poria, and Erik Cambria. 2018.\n“Recent Trends in Deep Learning Based Natural Language Processing\n[Review Article].” IEEE Comput. Intell.\nMag. 13 (3): 55–75. https://doi.org/10.1109/mci.2018.2840738.\n\n\nYu, Yuan, Martı́n Abadi, Paul Barham, Eugene Brevdo, Mike Burrows, Andy\nDavis, Jeff Dean, et al. 2018. “Dynamic Control Flow in\nLarge-Scale Machine Learning.” In Proceedings of the\nThirteenth EuroSys Conference, 265–83. ACM. https://doi.org/10.1145/3190508.3190551.\n\n\nZafrir, Ofir, Guy Boudoukh, Peter Izsak, and Moshe Wasserblat. 2019.\n“Q8BERT: Quantized 8Bit\nBERT.” In 2019 Fifth Workshop on Energy\nEfficient Machine Learning and Cognitive Computing - NeurIPS Edition\n(EMC2-NIPS), 36–39. IEEE; IEEE. https://doi.org/10.1109/emc2-nips53020.2019.00016.\n\n\nZeiler, Matthew D. 2012. “ADADELTA: An Adaptive Learning Rate\nMethod,” December, 119–49. https://doi.org/10.1002/9781118266502.ch6.\n\n\nZennaro, Marco, Brian Plancher, and V Janapa Reddi. 2022.\n“TinyML: Applied AI for\nDevelopment.” In The UN 7th Multi-Stakeholder Forum on\nScience, Technology and Innovation for the Sustainable Development\nGoals, 2022–05.\n\n\nZhang, Chen, Peng Li, Guangyu Sun, Yijin Guan, Bingjun Xiao, and Jason\nOptimizing Cong. 2015. “FPGA-Based Accelerator Design\nfor Deep Convolutional Neural Networks Proceedings of the 2015\nACM.” In SIGDA International Symposium on\nField-Programmable Gate Arrays-FPGA, 15:161–70.\n\n\nZhang, Dan, Safeen Huda, Ebrahim Songhori, Kartik Prabhu, Quoc Le, Anna\nGoldie, and Azalia Mirhoseini. 2022. “A Full-Stack Search\nTechnique for Domain Optimized Deep Learning Accelerators.” In\nProceedings of the 27th ACM International Conference on\nArchitectural Support for Programming Languages and Operating\nSystems, 27–42. ASPLOS ’22. New York, NY, USA: ACM. https://doi.org/10.1145/3503222.3507767.\n\n\nZhang, Dongxia, Xiaoqing Han, and Chunyu Deng. 2018. “Review on\nthe Research and Practice of Deep Learning and Reinforcement Learning in\nSmart Grids.” CSEE Journal of Power and Energy Systems 4\n(3): 362–70. https://doi.org/10.17775/cseejpes.2018.00520.\n\n\nZhang, Hongyu. 2008. “On the Distribution of Software\nFaults.” IEEE Trans. Software Eng. 34 (2): 301–2. https://doi.org/10.1109/tse.2007.70771.\n\n\nZhang, Jeff Jun, Tianyu Gu, Kanad Basu, and Siddharth Garg. 2018.\n“Analyzing and Mitigating the Impact of Permanent Faults on a\nSystolic Array Based Neural Network Accelerator.” In 2018\nIEEE 36th VLSI Test Symposium (VTS), 1–6. IEEE; IEEE. https://doi.org/10.1109/vts.2018.8368656.\n\n\nZhang, Jeff, Kartheek Rangineni, Zahra Ghodsi, and Siddharth Garg. 2018.\n“ThUnderVolt: Enabling Aggressive\nVoltage Underscaling and Timing Error Resilience for Energy Efficient\nDeep Learning Accelerators.” In 2018 55th ACM/ESDA/IEEE\nDesign Automation Conference (DAC), 1–6. IEEE. https://doi.org/10.1109/dac.2018.8465918.\n\n\nZhang, Li Lyna, Yuqing Yang, Yuhang Jiang, Wenwu Zhu, and Yunxin Liu.\n2020. “Fast Hardware-Aware Neural Architecture Search.” In\n2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition\nWorkshops (CVPRW). IEEE. https://doi.org/10.1109/cvprw50498.2020.00354.\n\n\nZhang, Qingxue, Dian Zhou, and Xuan Zeng. 2017. “Highly Wearable\nCuff-Less Blood Pressure and Heart Rate Monitoring with Single-Arm\nElectrocardiogram and Photoplethysmogram Signals.” BioMedical\nEngineering OnLine 16 (1): 23. https://doi.org/10.1186/s12938-017-0317-z.\n\n\nZhang, Tunhou, Hsin-Pai Cheng, Zhenwen Li, Feng Yan, Chengyu Huang, Hai\nHelen Li, and Yiran Chen. 2020. “AutoShrink:\nA Topology-Aware NAS for Discovering Efficient\nNeural Architecture.” In The Thirty-Fourth AAAI Conference on\nArtificial Intelligence, AAAI 2020, the Thirty-Second Innovative\nApplications of Artificial Intelligence Conference, IAAI 2020, the Tenth\nAAAI Symposium on Educational Advances in Artificial Intelligence, EAAI\n2020, New York, NY, USA, February 7-12, 2020, 6829–36. AAAI Press.\nhttps://aaai.org/ojs/index.php/AAAI/article/view/6163.\n\n\nZhao, Mark, and G. Edward Suh. 2018. “FPGA-Based\nRemote Power Side-Channel Attacks.” In 2018 IEEE Symposium on\nSecurity and Privacy (SP), 229–44. IEEE; IEEE. https://doi.org/10.1109/sp.2018.00049.\n\n\nZhao, Yue, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas\nChandra. 2018. “Federated Learning with Non-Iid Data.”\nArXiv Preprint abs/1806.00582. https://arxiv.org/abs/1806.00582.\n\n\nZhou, Bolei, Yiyou Sun, David Bau, and Antonio Torralba. 2018.\n“Interpretable Basis Decomposition for Visual Explanation.”\nIn Proceedings of the European Conference on Computer Vision\n(ECCV), 119–34.\n\n\nZhou, Chuteng, Fernando Garcia Redondo, Julian Büchel, Irem Boybat,\nXavier Timoneda Comas, S. R. Nandakumar, Shidhartha Das, Abu Sebastian,\nManuel Le Gallo, and Paul N. Whatmough. 2021.\n“AnalogNets: Ml-hw\nCo-Design of Noise-Robust TinyML Models and Always-on\nAnalog Compute-in-Memory Accelerator.” https://arxiv.org/abs/2111.06503.\n\n\nZhou, Peng, Xintong Han, Vlad I. Morariu, and Larry S. Davis. 2018.\n“Learning Rich Features for Image Manipulation Detection.”\nIn 2018 IEEE/CVF Conference on Computer Vision and Pattern\nRecognition, 1053–61. IEEE. https://doi.org/10.1109/cvpr.2018.00116.\n\n\nZhu, Hongyu, Mohamed Akrout, Bojian Zheng, Andrew Pelegris, Anand\nJayarajan, Amar Phanishayee, Bianca Schroeder, and Gennady Pekhimenko.\n2018. “Benchmarking and Analyzing Deep Neural Network\nTraining.” In 2018 IEEE International Symposium on Workload\nCharacterization (IISWC), 88–100. IEEE; IEEE. https://doi.org/10.1109/iiswc.2018.8573476.\n\n\nZhu, Ligeng, Lanxiang Hu, Ji Lin, Wei-Ming Chen, Wei-Chen Wang, Chuang\nGan, and Song Han. 2023. “PockEngine:\nSparse and Efficient Fine-Tuning in a Pocket.” In\n56th Annual IEEE/ACM International Symposium on\nMicroarchitecture. ACM. https://doi.org/10.1145/3613424.3614307.\n\n\nZhuang, Fuzhen, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, Hengshu\nZhu, Hui Xiong, and Qing He. 2021. “A Comprehensive Survey on\nTransfer Learning.” Proc. IEEE 109 (1): 43–76. https://doi.org/10.1109/jproc.2020.3004555.\n\n\nZoph, Barret, and Quoc V. Le. 2016. “Neural Architecture Search\nwith Reinforcement Learning,” November, 367–92. https://doi.org/10.1002/9781394217519.ch17.",
     "crumbs": [
       "REFERENCES",
       "References"