Quantifying the Computational Capability of a Nanomagnetic Reservoir Computing Platform with Emergent Magnetization Dynamics

• URL: http://arxiv.org/abs/2111.14603v1
• Date: Mon, 29 Nov 2021 15:35:39 GMT
• Title: Quantifying the Computational Capability of a Nanomagnetic Reservoir Computing Platform with Emergent Magnetization Dynamics
• Authors: Ian T Vidamour, Matthew O A Ellis, David Griffin, Guru Venkat, Charles Swindells, Richard W S Dawidek, Thomas J Broomhall, Nina-Juliane Steinke, Joshaniel F K Cooper, Francisco Maccherozzi, Sarnjeet S Dhesi, Susan Stepney, Eleni Vasilaki, Dan A Allwood, Thomas J Hayward
• Abstract summary: Arrays of interconnected magnetic nano-rings with emergent magnetization dynamics have been proposed for use in reservoir computing applications. We show that such reservoirs can be optimised for classification tasks by tuning hyper parameters. We then show that these metrics can be further improved by expanding the reservoir's output to include multiple, concurrent measures of the ring arrays' magnetic states.
• Score: 1.7042411355890392
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 can be further improved by expanding the reservoir's output to include multiple, concurrent measures of the ring arrays' magnetic states.

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