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Device-aware inference operations in SONOS nonvolatile memory arrays

Bennett, Christopher H.; Xiao, Tianyao X.; Dellana, Ryan A.; Feinberg, Benjamin F.; Agarwal, Sapan A.; Marinella, Matthew J.; Agrawal, Vineet; Prabhakar, Venkatraman; Ramkumar, Krishnaswamy; Hinh, Long; Saha, Swatilekha; Raghavan, Vijay; Chettuvetty, Ramesh

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.