Gradient-based optimization of spintronic devices

TL;DR

This paper introduces a gradient descent-based method for optimizing the physical parameters of spin-torque oscillators, enabling better simulation-experiment correspondence and high-accuracy image recognition tasks.

Contribution

It presents a novel application of automatic differentiation and gradient descent to optimize spintronic device parameters, improving upon traditional grid search methods.

Findings

Successfully tuned oscillator parameters to reproduce simulated dynamics.

Achieved high-accuracy image recognition using coupled spin-torque oscillators.

Demonstrated potential for designing spintronic systems for computational tasks.

Abstract

The optimization of physical parameters serves various purposes, such as system identification and efficiency in developing devices. Spin-torque oscillators have been applied to neuromorphic computing experimentally and theoretically, but the optimization of their physical parameters has usually been done by grid search. In this paper, we propose a scheme to optimize the parameters of the dynamics of macrospin-type spin-torque oscillators using the gradient descent method with automatic differentiation. First, we prepared numerically created dynamics as teacher data and successfully tuned the parameters to reproduce the dynamics. This can be applied to obtain the correspondence between the simulation and experiment of the spin-torque oscillators. Next, we successfully solved the image recognition task with high accuracy by connecting the coupled system of spin-torque oscillators to the input and output layers and training all of them through gradient descent. This approach allowed us to estimate how to control the experimental setup and design the physical systems so that the task could be solved with a high accuracy using spin-torque oscillators.