Resist: Robust Network Training for Memristive Crossbar-Based Neuromorphic Computing Systems

In recent years, memristive crossbar-based neuromorphic computing systems (NCS) have provided a promising solution for neural network acceleration. However, stuck-at faults(SAFs) and process variations in memristor devices significantly degrade the computing accuracy of NCS. In this brief, we propose a unified robust network training framework for a memristive crossbar-based NCS, simultaneously taking the impacts of SAFs and variations into account. In order to incorporate SAFs and variations into the training process, an effective sampling strategy for SAF and an efficient variation injection technique based on the local reparameterization method are developed. Experimental results clearly demonstrate that the proposed training framework can boost the computation accuracy of NCS.