Enhanced regularization for on-chip training using analog and temporary memory weights.

Abstract

In-memory computing techniques are used to accelerate artificial neural network (ANN) training and inference tasks. Memory technology and architectural innovations allow efficient matrix-vector multiplications, gradient calculations, and updates to network weights. However, on-chip learning for edge devices is quite challenging due to the frequent updates. Here, we propose using an analog and temporary on-chip memory (ATOM) cell with controllable retention timescales for implementing the weights of an on-chip training task. Measurement results for Read-Write timescales are presented for an ATOM cell fabricated in GlobalFoundries' 45 nm RFSOI technology. The effect of limited retention and its variability is evaluated for training a fully connected neural network with a variable number of layers for the MNIST hand-written digit recognition task. Our studies show that weight decay due to temporary memory can have benefits equivalent to regularization, achieving a ∼33% reduction in the validation error (from 3.6% to 2.4%). We also show that the controllability of the decay timescale can be advantageous in achieving a further ∼26% reduction in the validation error. This strongly suggests the utility of temporary memory during learning before on-chip non-volatile memories can take over for the storage and inference tasks using the neural network weights. We thus propose an algorithm-circuit codesign in the form of temporary analog memory for high-performing on-chip learning of ANNs.