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Poor Man's Training on MCUs: A Memory-Efficient Quantized Back-Propagation-Free Approach by Zhao, Yequan; Li, Hai; Young, Ian; Zhang, Zheng is a scholarly article available to read on EtoBox.

What is Poor Man's Training on MCUs: A Memory-Efficient Quantized Back-Propagation-Free Approach about?

Back propagation (BP) is the default solution for gradient computation in neural network training. However, implementing BP-based training on various edge devices such as FPGA, microcontrollers (MCUs), and analog computing platforms face multiple major challenges, such as the lack of hardware resources, long time-to-market, and dramatic errors in a low-precision setting. This paper presents a simple BP-free training scheme on an MCU, which makes edge training hardware design as easy as inference hardware design. We adopt a quantized zeroth-order method to estimate the gradients of quantized model parameters, which can overcome the error of a straight-through estimator in a low-precision BP scheme. We further employ a few dimension reduction methods (e.g., node perturbation, sparse training) to improve the convergence of zeroth-order training. Experiment results show that our BP-free training achieves comparable performance as BP-based training on adapting a pre-trained image classifier to various corrupted data on resource-constrained edge devices (e.g., an MCU with 1024-KB SRAM for dense full-model training, or an MCU with 256-KB SRAM for sparse training). This method is most suit

Author

Zhao, Yequan; Li, Hai; Young, Ian; Zhang, Zheng

Published

2024

Language

EN