The Impact of Resistance Drift of Phase Change Memory (PCM) Synaptic Devices on Artificial Neural Network Performance

Abstract

Phase change memory (PCM) has been considered as one of the most promising emerging non-volatile memories for in-memory computing of neural networks. In this letter, we investigate the impact of resistance drift and its statistical variations on two widely-used artificial neural network (ANN) models, multi-layer perceptron (MLP), and convolutional neural network (CNN). We employ experimentally measured resistance drift characteristics into the ANN models to accurately model weight updates represented by PCM synaptic devices. Our results suggest that the resistance drift in PCM causes minor accuracy degradation (only ~1%) for both MLP and CNN models. However, classification accuracy can be significantly reduced if the PCM drift characteristics exhibit high device-to-device and cycle-to-cycle variations in the drift coefficients.