Macromagnetic simulation for reservoir computing utilizing spin dynamics in magnetic tunnel junctions

TL;DR

This paper demonstrates that magnetic tunnel junctions can be used as effective reservoirs for computing by leveraging their spin dynamics, non-linearity, and memory effects, enabling high-performance reservoir computing with few devices.

Contribution

The study shows that MTJs exhibit the necessary properties for reservoir computing and can achieve high performance with fewer devices than traditional networks.

Findings

MTJs possess non-linearity and memory effects suitable for RC

High performance achieved with 5-7 MTJs, comparable to larger echo-state networks

No magnetic or electrical interactions needed between magnetizations

Abstract

The figures-of-merit for reservoir computing (RC), using spintronics devices called magnetic tunnel junctions (MTJs), are evaluated. RC is a type of recurrent neural network. The input information is stored in certain parts of the reservoir, and computation can be performed by optimizing a linear transform matrix for the output. While all the network characteristics should be controlled in a general recurrent neural network, such optimization is not necessary for RC. The reservoir only has to possess a non-linear response with memory effect. In this paper, macromagnetic simulation is conducted for the spin-dynamics in MTJs, for reservoir computing. It is determined that the MTJ-system possesses the memory effect and non-linearity required for RC. With RC using 5-7 MTJs, high performance can be obtained, similar to an echo-state network with 20-30 nodes, even if there are no magnetic and/or electrical interactions between the magnetizations.