Time series prediction capability of coupled spin Hall oscillator array-based reservoir computing

Abstract

Reservoir computing, a machine learning paradigm derived from recurrent neural networks, is efficient for complex time-series prediction tasks. Its advantages, including reduced computational requirements for weights, real-time prediction capability, and the use of physical nonlinear dynamical systems, make it well-suited for edge computing. However, acquiring the transient-state responses of reservoirs using multiple physical nodes requires substantial memory storage and numerous recording channels, which hinders dense integration and usage in memory-constrained devices. In this work, we present a frequency domain multiplexing approach to effectively utilize the collective dynamics of a coupled spin Hall oscillator array using a single output node. We investigate the collective behavior of the coupled oscillators through micromagnetic simulations and analyze reservoir states in both the time domain and Fourier space using Mackey-Glass inputs. Our results indicate that spectral analysis in the Fourier domain enhances reservoir performance, offering a promising strategy for data processing in physically constrained reservoir computing systems.