ADI’s MAX78000/MAX78002 project is comprised of five main repositories:
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This repo: Documentation
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The software development kit (MSDK), which contains drivers and example programs ready to run on the evaluation kits:
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The training repository, which is used for deep learning model development and training:
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The synthesis repository, which is used to convert a trained model into C code using the “izer” tool:
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The reference design repository, which contains host applications and sample applications for reference designs Note - Examples for EVkits and Feather boards are part of the MSDK
- Episode 1 - Who Needs Machine Learning Anyways?
- Episode 2 - Thinking About Machine Learning
- Episode 3 - All About Models
- Episode 4 - All About Training
- Episode 5 - Say Hello to the MAX78000
- Episode 6 - From Checkpoint to C
- Episode 7 - How to Train a Network
- Episode 8 - The MAX78000FTHR
- Episode 8.5 - Visual Studio Code
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MAX78000
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Link to IC description and datasheet: MAX78000
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Link to IC User Guide: MAX78000 User Guide.pdf
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Link to MAX78000 EV Kit description and documentation: MAX78000 EV Kit
- (Older revision) Link to Rev A EV Kit schematic: MAX78000 REV A including mods.pdf
- (Older revision) Link to Rev B EV Kit schematic: MAX78000 REV B.pdf
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Link to MAX78000 Feather Board description and documentation: MAX78000 Feather Board
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Link to MAX78000 Reference Design documentation and reference design application code repository: MAX78000 Reference Designs
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MAX78002
- Link to IC description and datasheet: MAX78002
- Link to IC User Guide: MAX78002 User Guide (Preliminary).pdf
- Link to MAX78002 EV Kit description and documentation: MAX78002 EV Kit
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MAX78000 and MAX78002
- Link to MSDK repository (for both MAX78000 and MAX78002): Analog Devices MSDK
- Link to MSDK documentation: MSDK Docs - click on index.html Note: HTML files are not rendered in GitHub and it is therefore recommended to view the MSDK documentation from a local copy, which is automatically installed with the MSDK.
- Quick Start Guides and Optional Features: Guides
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Developing Power-optimized Applications on the MAX78000
Abstract: Power consumption is a key factor for edge Artificial Intelligence (AI) applications where the entire system is powered by small battery cells and is expected to operate for months without recharging or replacing the batteries. The MAX78000 ultra-low power AI microcontroller is built to target such applications at the edge of the IoT. In this document, various configurations are described to enable users to develop power-optimized applications on the MAX78000, along with benchmarking examples. The power optimization methods are applied to two case study applications—Keyword Spotting with 20 keywords (KWS20) and Face Identification (FaceID), and the reported results can be used as a guideline for the user’s application.
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Face Identification Using MAX78000
Abstract: The MAX78000 is an ultra-low power Convolutional Neural Network (CNN) inference engine to run Artificial Intelligence (AI) computations on tiny edges of IoT. Yet the device can execute many complex networks to achieve critical and popular applications. This document describes an approach for Face Identification (FaceID) running on the MAX78000 where the model is built with ADI’s development flow on PyTorch, trained with different open datasets and deployed on the MAX78000 evaluation board.
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Keywords Spotting Using the MAX78000
Abstract: Audio assistants have become very popular with range of applications from household to automotive and industrial products and IoT. Such devices constantly listen to their surroundings and wake up on pretrained keywords to execute certain commands. Power consumption is a key factor for many of such resource constrained edge applications, where the connectivity to the cloud for processing of raw data is not feasibly. The MAX78000 is a new breed of Artificial Intelligence (AI) microcontroller built to enable neural networks to execute at ultra-low power and live at the edge of the IoT. In this document, we show case the implementation of a keyword spotting application on the MAX78000. The machine learning model is built with ADI’s development flow on PyTorch, trained with a subset of Google’s speech command dataset with 20 keywords, and deployed on the MAX78000EVKIT.
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Data Loader Design for MAX78000 Model Training
Abstract: The MAX78000, Artificial Intelligence Microcontroller with Ultra-Low-Power Convolutional Neural Network Accelerator, can effectively run artificial intelligence models on the chip. Users should first develop a neural network model, using Analog Devices’s development flow on PyTorch. The MAX78000 synthesizer tool then accepts the PyTorch checkpoint and the model description in the YAML format to automatically generate the C code to be compiled and executed on the MAX78000. One of the essential software components used in the model development phase is the data loader, which is responsible for application-specific data preparation tasks. This document describes principles and design considerations on a data loader implementation when preparing application-specific training and validation/test set entities suited for the MAX78000 model training.
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Developing Power-Optimized Applications on MAX78002 (Prerelease)
Abstract: Power consumption is a key factor for edge AI applications, where the entire system is powered by small battery cells and is expected to operate for months without recharging or replacing the batteries. The MAX78002 ultra-low power AI microcontroller is built to target such applications at the edge of the internet-of-things (IoT). This document describes various options to develop power-optimized applications on the MAX78002 and presents benchmarking examples.
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Utilizing Multiple CNN Models in One Application Using MAX78000 and MAX78002 (Prerelease)
Abstract: The MAX78000 and MAX78002 are Artificial Intelligence (AI) microcontrollers with an ultra-low power Convolutional Neural Network (CNN) inference engine to run AI edge applications on a battery-powered IoT device. The device can execute many complex CNN networks to achieve critical performance. This document describes an approach to utilize multiple CNN models in one application.
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L^3^U-net: Low-Latency Lightweight U-net Based Image Segmentation Model for Parallel CNN Processors
Abstract: In this research, we propose a tiny image segmentation model, L^3^U-net, that works on low-resource edge devices in real-time. We introduce a data folding technique that reduces inference latency by leveraging the parallel convolutional layer processing capability of the CNN accelerators. We also deploy the proposed model to such a device, MAX78000, and the results show that L^3^U-net achieves more than 90% accuracy over two different segmentation datasets with 10 fps.
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Ultra-Low Power Keyword Spotting at the Edge
Abstract: Keyword spotting (KWS) has become an indispensable part of many intelligent devices surrounding us, as audio is one of the most efficient ways of interacting with these devices. The accuracy and performance of KWS solutions have been the main focus of the researchers, and thanks to deep learning, substantial progress has been made in this domain. However, as the use of KWS spreads into IoT devices, energy efficiency becomes a very critical requirement besides the performance. We believe KWS solutions that would seek power optimization both in the hardware and the neural network (NN) model architecture are advantageous over many solutions in the literature where mostly the architecture side of the problem is considered. In this work, we designed an optimized KWS CNN model by considering end-to-end energy efficiency for the deployment at MAX78000, an ultra-low-power CNN accelerator. With the combined hardware and model optimization approach, we achieve 96.3% accuracy for 12 classes while only consuming 251 µJ per inference. We compare our results with other small-footprint neural network-based KWS solutions in the literature. Additionally, we share the energy consumption of our model in power-optimized ARM Cortex-M4F to depict the effectiveness of the chosen hardware for the sake of clarity.
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BED: A Real-Time Object Detection System for Edge Devices
Abstract: Deploying deep neural networks (DNNs) on edge devices provides efficient and effective solutions for the real-world tasks. Edge devices have been used for collecting a large volume of data efficiently in different domains. DNNs have been an effective tool for data processing and analysis. However, designing DNNs on edge devices is challenging due to the limited computational resources and memory. To tackle this challenge, we demonstrate object detection system for Edge Devices (BED) on the MAX78000 DNN accelerator. It integrates on-device DNN inference with a camera and an LCD display for image acquisition and detection exhibition, respectively. BED is a concise, effective and detailed solution, including model training, quantization, synthesis and deployment. The entire repository is open-sourced on Github , including a Graphical User Interface (GUI) for on-chip debugging. Experiment results indicate that BED can produce accurate detection with a 300-KB tiny DNN model, which takes only 91.9 ms of inference time and 1.845 mJ of energy.
Interested in the MAX78000/MAX78002, or just getting started with Machine Learning? Here’s a set of videos and books to get you going.