Hybrid Magnonic Reservoir Computing

TL;DR

This paper explores the use of magnonic systems as physical reservoir computers, introducing new designs with neural networks and spin wave guides, demonstrating competitive performance on real-world data sets.

Contribution

It presents novel magnonic reservoir computing architectures, including a neural network integrated design and a spin wave guide approach, validated through simulations.

Findings

Designs perform comparably or better than traditional neural networks

Simulations conducted using Magnum.np software

Effective processing of various real-world data sets

Abstract

Magnonic systems have been a major area of research interest due to their potential benefits in speed and lower power consumption compared to traditional computing. One particular area that they may be of advantage is as Physical Reservoir Computers in machine learning models. In this work, we build on an established design for using an Auto-Oscillation Ring as a reservoir computer by introducing a simple neural network midstream and introduce an additional design using a spin wave guide with a scattering regime for processing data with different types of inputs. We simulate these designs on the new micro magnetic simulation software, Magnum.np, and show that the designs are capable of performing on various real world data sets comparably or better than traditional dense neural networks.