Ellis, M.O.A. orcid.org/0000-0003-0338-8920, Welbourne, A., Kyle, S.J. et al. (4 more authors) (2023) Machine learning using magnetic stochastic synapses. Neuromorphic Computing and Engineering, 3 (2). 021001. ISSN 2634-4386
Abstract
The impressive performance of artificial neural networks has come at the cost of high energy usage and CO2 emissions. Unconventional computing architectures, with magnetic systems as a candidate, have potential as alternative energy-efficient hardware, but, still face challenges, such as stochastic behaviour, in implementation. Here, we present a methodology for exploiting the traditionally detrimental stochastic effects in magnetic domain-wall motion in nanowires. We demonstrate functional binary stochastic synapses alongside a gradient learning rule that allows their training with applicability to a range of stochastic systems. The rule, utilising the mean and variance of the neuronal output distribution, finds a trade-off between synaptic stochasticity and energy efficiency depending on the number of measurements of each synapse. For single measurements, the rule results in binary synapses with minimal stochasticity, sacrificing potential performance for robustness. For multiple measurements, synaptic distributions are broad, approximating better-performing continuous synapses. This observation allows us to choose design principles depending on the desired performance and the device's operational speed and energy cost. We verify performance on physical hardware, showing it is comparable to a standard neural network.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 The Author(s). Published by IOP Publishing Ltd. Original Content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI: https://creativecommons.org/licenses/by/4.0/ |
Keywords: | neuromorphic; magnetic nanowire; binary stochastic synapses; gradient rule; spintronics |
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) |
Funding Information: | Funder Grant number ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL EP/S009647/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 27 Jun 2023 11:11 |
Last Modified: | 27 Jun 2023 11:14 |
Status: | Published |
Publisher: | IOP Publishing |
Refereed: | Yes |
Identification Number: | 10.1088/2634-4386/acdb96 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:200909 |
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