Machine Learning

tinyML Summit 2022: Automating Model Optimization for Efficient Edge AI: from automated solutions…



tinyML Summit 2022
tinyML AutoML Session
Automating Model Optimization for Efficient Edge AI: from automated solutions to open-source toolkit
Dave CHENG, Senior Deep Learning Researcher, Qualcomm AI Research

Edge devices including smartphones, IoT devices typically operate with stringent power and thermal budget. Running deep neural networks (DNNs) on such edge devices is extremely challenging due to DNN’s need for high memory, compute, and energy. While significant research has been dedicated to optimizing DNNs, it is still an ongoing challenge to provide techniques and tools that automate DNN optimization in user-friendly manner.
In this talk, we present the leading techniques for the automated design of DNN for edge devices. Starting from our research work “Distilling Optimal Neural Network Architectures (DONNA)” on hardware-aware neural architectures search (NAS), we show that neural architectures can be effectively shrunk to improve latency while maintaining accuracy. Furthermore, we provide automated quantization methods to enable energy efficient fixed-point inference of these optimized models. We also discuss our open-source projects such as the AI Model Efficiency Toolkit (AIMET) and AIMET Model Zoo that close the gap between research and practically useful tools, thus enabling AI community to meet efficient edge inference needs.

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