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

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

Training an Ising Machine with Equilibrium Propagation

Ising machines, which are hardware implementations of the Ising model of coupled spins, have been influential in the development of unsupervised learning algorithms at the origins of Artificial Intelligence (AI). However, their application to AI has been limited due to the complexities in matching supervised training methods with Ising machine physics, even though these methods are essential for achieving high accuracy. In this study, we demonstrate a novel approach to train Ising machines in a supervised way through the Equilibrium Propagation algorithm, achieving comparable results to software-based implementations. We employ the quantum annealing procedure of the D-Wave Ising machine to train a fully-connected neural network on the MNIST dataset. Furthermore, we demonstrate that the machine's connectivity supports convolution operations, enabling the training of a compact convolutional network with minimal spins per neuron. Our findings establish Ising machines as a promising trainable hardware platform for AI, with the potential to enhance machine learning applications

Critical velocity for the vortex core reversal in perpendicular bias magnetic field

For a circular magnetic nanodot in a vortex ground state we study how the critical velocity $v_c$ of the vortex core reversal depends on the magnitude $H$ of a bias magnetic field applied perpendicularly to the dot plane. We find that, similarly to the case $H$ = 0, the critical velocity does not depend on the size of the dot. The critical velocity is dramatically reduced when the negative (i.e. opposite to the vortex core direction) bias field approaches the value, at which a \emph{static} core reversal takes place. A simple analytical model shows good agreement with our numerical result.Comment: 4 pages, 2 figure