Improving DNN Fault Tolerance using Weight Pruning and Differential Crossbar Mapping for ReRAM-based Edge AI

TL;DR

This paper proposes a novel weight pruning and differential crossbar mapping approach to enhance fault tolerance in ReRAM-based DNN accelerators, achieving higher failure tolerance without extra hardware or device-specific optimization.

Contribution

It introduces a universal fault-tolerant mapping scheme for ReRAM-based DNNs that does not require device-specific optimization or additional hardware.

Findings

Tolerates nearly ten times higher failure rates than traditional methods.

Does not increase hardware cost compared to existing schemes.

Universal applicability across different DNN tasks.

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.