Overcoming device unreliability with continuous learning in a population coding based computing system

TL;DR

This paper demonstrates that a population coding computing system using magnetic tunnel junctions can achieve robustness to device unreliability through continuous learning, balancing power and precision.

Contribution

It introduces a population coding system with magnetic tunnel junctions that employs continuous learning to recover from neuron loss and unreliable synapses.

Findings

Continuous learning enables recovery from neuron loss.

Unreliable synaptic weights can be used with minimal performance loss.

Optimal power consumption is achieved by balancing neuron count and weight energy barriers.

Abstract

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.