Ternary Stochastic Neuron -- Implemented with a Single Strained Magnetostrictive Nanomagnet

TL;DR

This paper introduces a novel implementation of a ternary stochastic neuron using a single strained magnetostrictive nanomagnet, enabling efficient multi-state neuromorphic computing with low energy consumption.

Contribution

It demonstrates how to realize a ternary stochastic neuron with a zero-energy-barrier magnetostrictive nanomagnet under strain, advancing hardware for neuromorphic applications.

Findings

Successful implementation of a TSN with a single strained nanomagnet

Potential for low-energy, multi-state neuromorphic hardware

Enhanced pattern classification capabilities

Abstract

Stochastic neurons are extremely efficient hardware for solving a large class of problems and usually come in two varieties -- "binary" where the neuronal statevaries randomly between two values of -1, +1 and "analog" where the neuronal state can randomly assume any value between -1 and +1. Both have their uses in neuromorphic computing and both can be implemented with low- or zero-energy-barrier nanomagnets whose random magnetization orientations in the presence of thermal noise encode the binary or analog state variables. In between these two classes is n-ary stochastic neurons, mainly ternary stochastic neurons (TSN) whose state randomly assumes one of three values (-1, 0, +1), which have proved to be efficient in pattern classification tasks such as recognizing handwritten digits from the MNIST data set or patterns from the CIFAR-10 data set. Here, we show how to implement a TSN with a zero-energy-barrier (shape isotropic) magnetostrictive nanomagnet subjected to uniaxial strain.