Vidamour, I.T., Ellis, M.O.A. orcid.org/0000-0003-0338-8920, Griffin, D. et al. (12 more authors) (Submitted: 2021) Quantifying the computational capability of a nanomagnetic reservoir computing platform with emergent magnetization dynamics. [Preprint]
Abstract
Devices based on arrays of interconnected magnetic nano-rings with emergent
magnetization dynamics have recently been proposed for use in reservoir
computing applications, but for them to be computationally useful it must be
possible to optimise their dynamical responses. Here, we use a phenomenological
model to demonstrate that such reservoirs can be optimised for classification
tasks by tuning hyperparameters that control the scaling and input rate of data
into the system using rotating magnetic fields. We use task-independent metrics
to assess the rings' computational capabilities at each set of these
hyperparameters and show how these metrics correlate directly to performance in
spoken and written digit recognition tasks. We then show that these metrics,
and performance in tasks, can be further improved by expanding the reservoir's
output to include multiple, concurrent measures of the ring arrays magnetic
states.
Metadata
Item Type: | Preprint |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 The Authors. Preprint made available under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Materials Science and Engineering (Sheffield) |
Funding Information: | Funder Grant number European Commission - HORIZON 2020 828985 Engineering and Physical Sciences Research Council EP/V006339/1; EP/J002275/1; EP/S009647/1; 2276907 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 21 Jun 2022 09:50 |
Last Modified: | 21 Jun 2022 09:50 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:187969 |