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Title: Poor Man’s Training on MCUs: A Memory-Efficient Quantized Back-Propagation-Free Approach
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 faces multiple major challenges, such as the lack of hardware resources, long time-to-market, and dramatic errors in a low-precision setting. This article 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 suitable for application scenarios where memory cost and time-to-market are the major concerns, but longer latency can be tolerated.  more » « less
Award ID(s):
2419889
PAR ID:
10704878
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
ACM Transactions on Design Automation of Electronic Systems
Date Published:
Journal Name:
ACM Transactions on Design Automation of Electronic Systems
Volume:
30
Issue:
5
ISSN:
1084-4309
Page Range / eLocation ID:
1 to 33
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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