Fault-free: A Fault-resilient Deep Neural Network Accelerator based on Realistic ReRAM Devices

Abstract

Energy-efficient Resistive RAM (ReRAM) based deep neural network (DNN) accelerator suffers from severe Stuck-At-Fault (SAF) problem that drastically degrades the inference accuracy. The SAF problem gets even worse in realistic ReRAM devices with low cell resolution. To address the issue, we propose a fault-resilient DNN accelerator based on realistic ReRAM devices. We first analyze the SAF problem in a realistic ReRAM device and propose a 3-stage offline fault- resilient compilation and lightweight online compensation. The proposed work enables the reliable execution of DNN with only 5% area and 0.8% energy overhead from the ideal ReRAM-based DNN accelerator.