Stochastic Spin-Orbit Torque Devices as Elements for Bayesian Inference

TL;DR

This paper demonstrates a spintronic device that leverages stochastic switching behavior to perform probabilistic inference, mimicking neural and cognitive functions for unconventional computing applications.

Contribution

It introduces a novel spintronic device that directly maps to stochastic neural units, enabling hardware-based Bayesian inference.

Findings

Probabilistic switching controlled by spin-orbit torque and thermal noise.

Device can perform probabilistic inference tasks.

Potential for hardware mimicking neural probabilistic behavior.

Abstract

Probabilistic inference from real-time input data is becoming increasingly popular and may be one of the potential pathways at enabling cognitive intelligence. As a matter of fact, preliminary research has revealed that stochastic functionalities also underlie the spiking behavior of neurons in cortical microcircuits of the human brain. In tune with such observations, neuromorphic and other unconventional computing platforms have recently started adopting the usage of computational units that generate outputs probabilistically, depending on the magnitude of the input stimulus. In this work, we experimentally demonstrate a spintronic device that offers a direct mapping to the functionality of such a controllable stochastic switching element. We show that the probabilistic switching of Ta/CoFeB/MgO heterostructures in presence of spin-orbit torque and thermal noise can be harnessed to enable probabilistic inference in a plethora of unconventional computing scenarios. This work can potentially pave the way for hardware that directly mimics the computational units of Bayesian inference.