Christopher H. Bennett,T. Patrick Xiao,Ryan Dellana,Vineet Agrawal,Ben Feinberg,Venkatraman Prabhakar,Krishnaswamy Ramkumar,Long Hinh,Swatilekha Saha,Vijay Raghavan,Ramesh Chettuvetty,Sapan Agarwal,Matthew J. Marinella
Abstract URL: https://arxiv.org/abs/2004.00802v1
Non-volatile memory arrays can deploy pre-trained neural network models for edge inference. However, these systems are affected by device-level noise and retention issues. Here, we examine damage caused by these effects, introduce a mitigation strategy, and demonstrate its use in fabricated array of SONOS (Silicon-Oxide-Nitride-Oxide-Silicon) devices. On MNIST, fashion-MNIST, and CIFAR-10 tasks, our approach increases resilience to synaptic noise and drift. We also show strong performance can be realized with ADCs of 5-8 bits precision.