Manuel Le Gallo
CIMTEC 2024
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 90 nm 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.
Manuel Le Gallo
CIMTEC 2024
Matthias Kaes, Manuel Le Gallo, et al.
Journal of Applied Physics
S. Sidler, Irem Boybat, et al.
ESSDERC 2016
Benedikt Kersting, Syed Ghazi Sarwat, et al.
Advanced Functional Materials