On the Asymmetry of Stuck-at Fault Sensitivity in Memristive Neural Architectures

The use of memristive crossbar-based architectures has gained traction as a potential solution for performing computationally expensive tasks such as matrix-vector multiplication and vector outer product, which require significant amount of space, time, and energy. Despite being deemed inherently fault-tolerant, memristive crossbar-based neural architecture (MCNA) may often experience accuracy degradation due to hardware faults, resulting in significant variations. This study aims to comprehensively analyze the impact of stuck-at faults (SAFs) on the accuracy of a neural network during classification or regression. Contrary to the popular belief, it is observed that the impact of stuck-at-0 (SA0) and stuck-at-1 (SA1) faults are highly asymmetric with respect to the loss of accuracy. Thus this study might help in planning test strategies for the enhancement of fault immunity in memristive neural architectures.