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ARiSE-Lab · Dec 3, 2023 · a04ea62 · a04ea62
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       title: Bringing Foundational Models to Consumer Devices via ML Compilation
       abstract: "Deploying deep learning models on various devices has become an important topic. Machine learning compilation is an emerging field that leverages compiler and automatic search techniques to accelerate AI models. ML compilation brings a unique set of challenges: emerging machine learning models; increasing hardware specialization brings a diverse set of acceleration primitives; growing tension between flexibility and performance.  In this talk. I then discuss our experience in bringing foundational models to a variety of devices and hardware environments through machine learning compilation."
       bio: "Tianqi Chen is currently an Assistant Professor at the Machine Learning Department and Computer Science Department of Carnegie Mellon University. He is also the Chief Technologist of OctoML. He received his PhD. from the Paul G. Allen School of Computer Science & Engineering at the University of Washington. He has created many major learning systems that are widely adopted: XGBoost, TVM, and MLC-LLM."
+      livestream: https://www.youtube.com/watch?v=InoNMvjs_vo
     - speaker: Dan Fu (Stanford, Together)
-      date: M 12/04/23
+      date: M 12/04/23
+      title: "Monarch Mixer: Making Foundation Models More Efficient"
+      abstract: "Machine learning models are increasingly being scaled in both sequence length and model dimension to reach longer contexts and better performance. However, existing architectures like Transformers scale quadratically along both these axes. In this talk I'll discuss Monarch Mixer (M2), a new architecture that uses the same sub-quadratic primitive along both sequence length and model dimension. M2 mixes information along the sequence and model dimensions using Monarch matrices, a simple class of expressive structured matrices that captures many linear transforms, achieves high hardware efficiency on GPUs, and scales sub-quadratically."
+      bio: "Dan Fu is a PhD student in the Computer Science Department at Stanford University, where he is co-advised by Christopher Ré and Kayvon Fatahalian. His research is at the intersection of systems and machine learning and focuses on developing algorithms and architectures to make machine learning more efficient."
+      livestream: https://www.youtube.com/watch?v=IS59IwGLvVs