The brain, which uses redundancy and continuous learning to overcome the unreliability of its components, provides a promising path to building computing systems that are robust to the unreliability of their constituent nanodevices. In this work, we illustrate this path by a computing system based on population coding with magnetic tunnel junctions that implement both neurons and synaptic weights. We show that equipping such a system with continuous learning enables it to recover from the loss of neurons and makes it possible to use unreliable synaptic weights (i.e. low energy barrier magnetic memories). There is a tradeoff between power consumption and precision because low energy barrier memories consume less energy than high barrier ones. For a given precision, there is an optimal number of neurons and an optimal energy barrier for the weights that leads to minimum power consumption.
@article{arxiv.1806.00399,
title = {Overcoming device unreliability with continuous learning in a population coding based computing system},
author = {Alice Mizrahi and Julie Grollier and Damien Querlioz and M. D. Stiles},
journal= {arXiv preprint arXiv:1806.00399},
year = {2018}
}