A Self-Test Framework for Detecting Fault-induced Accuracy Drop in Neural Network Accelerators

Abstract

Hardware accelerators built with SRAM or emerging memory devices are essential to the accommodation of the ever-increasing Deep Neural Network (DNN) workloads on resource-constrained devices. After deployment, however, the performance of these accelerators is threatened by the faults in their on-chip and off-chip memories where millions of DNN weights are held. Different types of faults may exist depending on the underlying memory technology, degrading inference accuracy. To tackle this challenge, this paper proposes an online self-test framework that monitors the accuracy of the accelerator with a small set of test images selected from the test dataset. Upon detecting a noticeable level of accuracy drop, the framework uses additional test images to identify the corresponding fault type and predict the severeness of faults by analyzing the change in the ranking of the test images. Experimental results show that our method can quickly detect the fault status of a DNN accelerator and provide accurate fault type and fault severeness information, allowing for subsequent recovery and self-healing process.