Anthony Debunne, Kumar Virwani, et al.
JES
Using two phase-change memory devices per synapse, a three-layer perceptron network with 164 885 synapses is trained on a subset (5000 examples) of the MNIST database of handwritten digits using a backpropagation variant suitable for nonvolatile memory (NVM) + selector crossbar arrays, obtaining a training (generalization) accuracy of 82.2% (82.9%). Using a neural network simulator matched to the experimental demonstrator, extensive tolerancing is performed with respect to NVM variability, yield, and the stochasticity, linearity, and asymmetry of the NVM-conductance response. We show that a bidirectional NVM with a symmetric, linear conductance response of high dynamic range is capable of delivering the same high classification accuracies on this problem as a conventional, software-based implementation of this same network.
Anthony Debunne, Kumar Virwani, et al.
JES
Stefano Ambrogio, Pritish Narayanan, et al.
AICAS 2020
Mathieu Mivelle, Pierre Viktorovitch, et al.
Optics Express
Pritish Narayanan, Alessandro Fumarola, et al.
IBM J. Res. Dev