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An Accurate, Error-Tolerant, and Energy-Efficient Neural Network Inference Engine Based on SONOS Analog Memory

IEEE Transactions on Circuits and Systems I: Regular Papers

Xiao, Tianyao P.; Feinberg, Benjamin; Bennett, Christopher; Agrawal, Vineet; Saxena, Prashant; Prabhakar, Venkatraman; Ramkumar, Krishnaswamy; Medu, Harsha; Raghavan, Vijay; Chettuvetty, Ramesh; Agarwal, Sapan; Marinella, Matthew

We demonstrate SONOS (silicon-oxide-nitride-oxide-silicon) analog memory arrays that are optimized for neural network inference. The devices are fabricated in a 40nm process and operated in the subthreshold regime for in-memory matrix multiplication. Subthreshold operation enables low conductances to be implemented with low error, which matches the typical weight distribution of neural networks, which is heavily skewed toward near-zero values. This leads to high accuracy in the presence of programming errors and process variations. We simulate the end-To-end neural network inference accuracy, accounting for the measured programming error, read noise, and retention loss in a fabricated SONOS array. Evaluated on the ImageNet dataset using ResNet50, the accuracy using a SONOS system is within 2.16% of floating-point accuracy without any retraining. The unique error properties and high On/Off ratio of the SONOS device allow scaling to large arrays without bit slicing, and enable an inference architecture that achieves 20 TOPS/W on ResNet50, a > 10× gain in energy efficiency over state-of-The-Art digital and analog inference accelerators.

More Details
TYPE Journal Article YEAR 2022
DOIOSTIScopus
Results 7426–7450 of 99,299
Results 7426–7450 of 99,299