Drift-Enhanced Unsupervised Learning of Handwritten Digits in Spiking Neural Network With PCM Synapses

Abstract

Phase change memory (PCM), one of the most mature emerging non-volatile memories, has gained considerable attention over the years for use as electronic synapses in biologically inspired neuromorphic systems. The resistance drift of PCM devices, nonetheless, has long been identified as one of the biggest challenges toward realizing many areas of applications. Although this drawback has been extensively studied for memory development and many methods were proposed to mitigate the drift effect, its impact, if any, on online learning has not been fully explored yet. In this letter, we investigate the impact of resistance drift and variations in resistance drift parameters during unsupervised online learning. We use the resistance drift characteristics measured from experiments and incorporate them into the spiking neural network (SNN) for MNIST handwritten digits classification. Our results show that resistance drift, considered as a non-ideality for PCM devices, can be exploited to boost accuracy for online learning of handwritten digits in the SNN.