Stochastic weight updates in phase-change memory-based synapses and their influence on artificial neural networks

Artificial neural networks (ANN) have become a
powerful tool for machine learning. Resistive memory devices can
be used for the realization of a non-von Neumann computational
platform for ANN training in an area-efficient way. For instance,
the conductance values of phase-change memory (PCM) devices
can be used to represent synaptic weights and can be updated
in-situ according to learning rules. However, non-ideal device
characteristics pose challenges to reach competitive classification
accuracies. In this paper, we investigate the impact of granularity
and stochasticity associated with the conductance changes on
ANN performance. Using a PCM prototype chip fabricated in
the 90nm technology node, we present a detailed experimental
characterization of the conductance changes. Simulations are
done in order to quantify the effect of the experimentally
observed conductance change granularity and stochasticity on
classification accuracies in a fully connected ANN trained with
backpropagation.