Testing for Multiple Faults in Deep Neural Networks

Hardware accelerators for deep neural network (DNN) workloads are vulnerable to various faults. This necessitates the development of efficient testing methodologies to detect faults in DNN accelerators. This article proposes a test pattern generation approach to detect fault patterns in DNN hardware accelerators.