Reconfigurable Reservoir Computing in a Magnetic Metamaterial

In-materia reservoir computing (RC) leverages the intrinsic physical responses of functional materials to perform complex computational tasks. Magnetic metamaterials are exciting candidates for RC due to their huge state space, nonlinear emergent dynamics, and non-volatile memory. However, to be sui...

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Published in:arXiv.org
Main Authors: Vidamour, Ian, Swindells, Charles, Guru Venkat, Manneschi, Luca, Fry, Paul, Welbourne, Alexander, Rowan-Robinson, Richard, Backes, Dirk, Maccherozzi, Francisco, Dhesi, Sarnjeet, Vasilaki, Eleni, Allwood, Daniel, Hayward, Thomas
Format: Paper
Language:English
Published: Ithaca Cornell University Library, arXiv.org 27.08.2023
Subjects:
Arrays
Complexity
Delay lines
Ferrous alloys
Magnetic alloys
Magnetic materials
Metamaterials
Recognition
Substrates
Time series
ISSN:2331-8422
Online Access:Get full text
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Summary:In-materia reservoir computing (RC) leverages the intrinsic physical responses of functional materials to perform complex computational tasks. Magnetic metamaterials are exciting candidates for RC due to their huge state space, nonlinear emergent dynamics, and non-volatile memory. However, to be suitable for a broad range of tasks, the material system is required to exhibit a broad range of properties, and isolating these behaviours experimentally can often prove difficult. By using an electrically accessible device consisting of an array of interconnected magnetic nanorings -- a system shown to exhibit complex emergent dynamics -- here we show how reconfiguring the reservoir architecture allows exploitation of different aspects the system's dynamical behaviours. This is evidenced through state-of-the-art performance in diverse benchmark tasks with very different computational requirements, highlighting the additional computational configurability that can be obtained by altering the input/output architecture around the material system.
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ISSN:2331-8422
DOI:10.48550/arxiv.2206.04446