Improving DNN Fault Tolerance using Weight Pruning and Differential Crossbar Mapping for ReRAM-based Edge AI
Abstract
Recent research demonstrated the promise of using resistive random access memory (ReRAM) as an emerging technology to perform inherently parallel analog domain in-situ matrix-vector multiplication -- the intensive and key computation in deep neural networks (DNNs). However, hardware failure, such as stuck-at-fault defects, is one of the main concerns that impedes the ReRAM devices to be a feasible solution for real implementations. The existing solutions to address this issue usually require an optimization to be conducted for each individual device, which is impractical for mass-produced products (e.g., IoT devices). In this paper, we rethink the value of weight pruning in ReRAM-based DNN design from the perspective of model fault tolerance. And a differential mapping scheme is proposed to improve the fault tolerance under a high stuck-on fault rate. Our method can tolerate almost an order of magnitude higher failure rate than the traditional two-column method in representative DNN tasks. More importantly, our method does not require extra hardware cost compared to the traditional two-column mapping scheme. The improvement is universal and does not require the optimization process for each individual device.
Cite
@article{arxiv.2106.09166,
title = {Improving DNN Fault Tolerance using Weight Pruning and Differential Crossbar Mapping for ReRAM-based Edge AI},
author = {Geng Yuan and Zhiheng Liao and Xiaolong Ma and Yuxuan Cai and Zhenglun Kong and Xuan Shen and Jingyan Fu and Zhengang Li and Chengming Zhang and Hongwu Peng and Ning Liu and Ao Ren and Jinhui Wang and Yanzhi Wang},
journal= {arXiv preprint arXiv:2106.09166},
year = {2021}
}
Comments
In Proceedings of the 22nd International Symposium on Quality Electronic Design (ISQED), 2021