Neuromorphic computation with a single magnetic domain wall

Abstract Machine learning techniques are commonly used to model complex relationships but implementations on digital hardware are relatively inefficient due to poor matching between conventional computer architectures and the structures of the algorithms they are required to simulate. Neuromorphic d...

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Autores principales:	Razvan V. Ababei, Matthew O. A. Ellis, Ian T. Vidamour, Dhilan S. Devadasan, Dan A. Allwood, Eleni Vasilaki, Thomas J. Hayward
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/311665638511400082e878163f25159a
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spelling	oai:doaj.org-article:311665638511400082e878163f25159a2021-12-02T16:35:36ZNeuromorphic computation with a single magnetic domain wall10.1038/s41598-021-94975-y2045-2322https://doaj.org/article/311665638511400082e878163f25159a2021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94975-yhttps://doaj.org/toc/2045-2322Abstract Machine learning techniques are commonly used to model complex relationships but implementations on digital hardware are relatively inefficient due to poor matching between conventional computer architectures and the structures of the algorithms they are required to simulate. Neuromorphic devices, and in particular reservoir computing architectures, utilize the inherent properties of physical systems to implement machine learning algorithms and so have the potential to be much more efficient. In this work, we demonstrate that the dynamics of individual domain walls in magnetic nanowires are suitable for implementing the reservoir computing paradigm in hardware. We modelled the dynamics of a domain wall placed between two anti-notches in a nickel nanowire using both a 1D collective coordinates model and micromagnetic simulations. When driven by an oscillating magnetic field, the domain exhibits non-linear dynamics within the potential well created by the anti-notches that are analogous to those of the Duffing oscillator. We exploit the domain wall dynamics for reservoir computing by modulating the amplitude of the applied magnetic field to inject time-multiplexed input signals into the reservoir, and show how this allows us to perform machine learning tasks including: the classification of (1) sine and square waves; (2) spoken digits; and (3) non-temporal 2D toy data and hand written digits. Our work lays the foundation for the creation of nanoscale neuromorphic devices in which individual magnetic domain walls are used to perform complex data analysis tasks.Razvan V. AbabeiMatthew O. A. EllisIan T. VidamourDhilan S. DevadasanDan A. AllwoodEleni VasilakiThomas J. HaywardNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Razvan V. Ababei Matthew O. A. Ellis Ian T. Vidamour Dhilan S. Devadasan Dan A. Allwood Eleni Vasilaki Thomas J. Hayward Neuromorphic computation with a single magnetic domain wall
description	Abstract Machine learning techniques are commonly used to model complex relationships but implementations on digital hardware are relatively inefficient due to poor matching between conventional computer architectures and the structures of the algorithms they are required to simulate. Neuromorphic devices, and in particular reservoir computing architectures, utilize the inherent properties of physical systems to implement machine learning algorithms and so have the potential to be much more efficient. In this work, we demonstrate that the dynamics of individual domain walls in magnetic nanowires are suitable for implementing the reservoir computing paradigm in hardware. We modelled the dynamics of a domain wall placed between two anti-notches in a nickel nanowire using both a 1D collective coordinates model and micromagnetic simulations. When driven by an oscillating magnetic field, the domain exhibits non-linear dynamics within the potential well created by the anti-notches that are analogous to those of the Duffing oscillator. We exploit the domain wall dynamics for reservoir computing by modulating the amplitude of the applied magnetic field to inject time-multiplexed input signals into the reservoir, and show how this allows us to perform machine learning tasks including: the classification of (1) sine and square waves; (2) spoken digits; and (3) non-temporal 2D toy data and hand written digits. Our work lays the foundation for the creation of nanoscale neuromorphic devices in which individual magnetic domain walls are used to perform complex data analysis tasks.
format	article
author	Razvan V. Ababei Matthew O. A. Ellis Ian T. Vidamour Dhilan S. Devadasan Dan A. Allwood Eleni Vasilaki Thomas J. Hayward
author_facet	Razvan V. Ababei Matthew O. A. Ellis Ian T. Vidamour Dhilan S. Devadasan Dan A. Allwood Eleni Vasilaki Thomas J. Hayward
author_sort	Razvan V. Ababei
title	Neuromorphic computation with a single magnetic domain wall
title_short	Neuromorphic computation with a single magnetic domain wall
title_full	Neuromorphic computation with a single magnetic domain wall
title_fullStr	Neuromorphic computation with a single magnetic domain wall
title_full_unstemmed	Neuromorphic computation with a single magnetic domain wall
title_sort	neuromorphic computation with a single magnetic domain wall
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/311665638511400082e878163f25159a
work_keys_str_mv	AT razvanvababei neuromorphiccomputationwithasinglemagneticdomainwall AT matthewoaellis neuromorphiccomputationwithasinglemagneticdomainwall AT iantvidamour neuromorphiccomputationwithasinglemagneticdomainwall AT dhilansdevadasan neuromorphiccomputationwithasinglemagneticdomainwall AT danaallwood neuromorphiccomputationwithasinglemagneticdomainwall AT elenivasilaki neuromorphiccomputationwithasinglemagneticdomainwall AT thomasjhayward neuromorphiccomputationwithasinglemagneticdomainwall
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Neuromorphic computation with a single magnetic domain wall

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