Fault-free: A Fault-resilient Deep Neural Network Accelerator based on Realistic ReRAM Devices
2021 58TH ACM/IEEE DESIGN AUTOMATION CONFERENCE (DAC)(2021)
摘要
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.
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关键词
SAF problem,fault-resilient DNN accelerator,3-stage offline fault-resilient compilation,ReRAM-based DNN accelerator,stuck-at-fault problem,energy-efficient resistive RAM,fault-resilient deep neural network accelerator
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