Binary weight network utilizing multi-functional spin-orbit torque device for image recognition

Abstract

Spintronic devices driven by spin–orbit torque (SOT) have a lot of potential for future neuromorphic computing hardware platforms. Nevertheless, problems such as multistate loss, weight time-dependent variability, and output discontinuity ensue when the device size is reduced to the nanoscale, which are contradictory to the requirements of traditional high-precision neural networks. In this study, we propose a high-precision all-spin neural network based on magnetization switching of a simple nanoscale multi-functional SOT device. The all-spin neural network is implemented by the Binary Weight Network, in which the binary synapse is encoded by the two states formed by the device's deterministic switching and the practical neuron is realized by its stochastic switching probability. Furthermore, we use a difference derivation training algorithm for the general-purpose network in a novel way to be more compatible with the discontinuous neuron output. Using this strategy, our network can reach ∼81.73 % recognition accuracy on the universal CIFAR-10 dataset, paving the way for future practical hardware implementation of nanoscale SOT devices in high-precision compact neuromorphic computing.