# Reservoir Computing on Spin-Torque Oscillator Array

## TL;DR

This paper demonstrates that spin-torque oscillator arrays can be effectively used for reservoir computing, with performance influenced by synchronization states and surpassing traditional echo-state networks in certain configurations.

## Contribution

It introduces the use of STO arrays for reservoir computing and shows their potential advantages over conventional neural network models.

## Key findings

- Reservoir computing is feasible with synchronized STO oscillations.
- Performance improves with more STOs and peaks at the boundary between synchronization and disorder.
- STO arrays outperform similar-sized echo-state networks in reservoir computing tasks.

## Abstract

We numerically study reservoir computing on a spin-torque oscillator (STO) array, describing the magnetization dynamics of the STO array by a nonlinear oscillator model. The STOs exhibit synchronized oscillation due to coupling by magnetic dipolar fields. We show that reservoir computing can be performed using the synchronized oscillation state. The performance can be improved by increasing the number of STOs. The performance becomes highest at the boundary between the synchronized and disordered states. Using an STO array, we can achieve higher performance than that of an echo-state network with similar number of units. This result indicates that STO arrays are promising for hardware implementation of reservoir computing.