Ternary stochastic neuron- implemented with a single strained magnetostrictive nanomagnet

Stochastic neurons are extremely efficient hardware for solving a large class of problems and usually come in two varieties - 'binary' where the neuronal state varies randomly between two values of ±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 isn-ary stochastic neurons, mainly ternary stochastic neurons (TSNs) 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.