MCUNet is a system-algorithm co-design framework for tiny deep learning on microcontrollers. It consists of TinyNAS and TinyEngine. They are co-designed to fit the tight memory budgets. With system-algorithm co-design, we can significantly improve the deep learning performance on the same tiny memory budget.
In this paper, we introduce the Hardware-Aware Automated Quantization (HAQ) framework which leverages the reinforcement learning to automatically determine the quantization policy, and we take the hardware accelerator's feedback in the design loop.
We introduce “deep compression”, a three stage pipeline: pruning, trained quantization and Huffman coding, that work together to reduce the storage requirement of neural networks by 35× to 49× without affecting their accuracy.
In MCUNetV3, we enable on-device training under 256KB SRAM and 1MB Flash, using less than 1/1000 memory of PyTorch while matching the accuracy on the visual wake words application. It enables the model to adapt to newly collected sensor data and users can enjoy customized services without uploading the data to the cloud thus protecting privacy.
