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Magnetic Elements for Neuromorphic Computing

Neuromorphic computing is assumed to be significantly more energy efficient than, and at the same time expected to outperform, conventional computers in several applications, such as data classification, since it overcomes the so-called von Neumann bottleneck. Artificial synapses and neurons can be...

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Detalles Bibliográficos
Autores principales: Blachowicz, Tomasz, Ehrmann, Andrea
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7321415/
https://www.ncbi.nlm.nih.gov/pubmed/32486173
http://dx.doi.org/10.3390/molecules25112550
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author Blachowicz, Tomasz
Ehrmann, Andrea
author_facet Blachowicz, Tomasz
Ehrmann, Andrea
author_sort Blachowicz, Tomasz
collection PubMed
description Neuromorphic computing is assumed to be significantly more energy efficient than, and at the same time expected to outperform, conventional computers in several applications, such as data classification, since it overcomes the so-called von Neumann bottleneck. Artificial synapses and neurons can be implemented into conventional hardware using new software, but also be created by diverse spintronic devices and other elements to completely avoid the disadvantages of recent hardware architecture. Here, we report on diverse approaches to implement neuromorphic functionalities in novel hardware using magnetic elements, published during the last years. Magnetic elements play an important role in neuromorphic computing. While other approaches, such as optical and conductive elements, are also under investigation in many groups, magnetic nanostructures and generally magnetic materials offer large advantages, especially in terms of data storage, but they can also unambiguously be used for data transport, e.g., by propagation of skyrmions or domain walls. This review underlines the possible applications of magnetic materials and nanostructures in neuromorphic systems.
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spelling pubmed-73214152020-06-29 Magnetic Elements for Neuromorphic Computing Blachowicz, Tomasz Ehrmann, Andrea Molecules Review Neuromorphic computing is assumed to be significantly more energy efficient than, and at the same time expected to outperform, conventional computers in several applications, such as data classification, since it overcomes the so-called von Neumann bottleneck. Artificial synapses and neurons can be implemented into conventional hardware using new software, but also be created by diverse spintronic devices and other elements to completely avoid the disadvantages of recent hardware architecture. Here, we report on diverse approaches to implement neuromorphic functionalities in novel hardware using magnetic elements, published during the last years. Magnetic elements play an important role in neuromorphic computing. While other approaches, such as optical and conductive elements, are also under investigation in many groups, magnetic nanostructures and generally magnetic materials offer large advantages, especially in terms of data storage, but they can also unambiguously be used for data transport, e.g., by propagation of skyrmions or domain walls. This review underlines the possible applications of magnetic materials and nanostructures in neuromorphic systems. MDPI 2020-05-30 /pmc/articles/PMC7321415/ /pubmed/32486173 http://dx.doi.org/10.3390/molecules25112550 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Blachowicz, Tomasz
Ehrmann, Andrea
Magnetic Elements for Neuromorphic Computing
title Magnetic Elements for Neuromorphic Computing
title_full Magnetic Elements for Neuromorphic Computing
title_fullStr Magnetic Elements for Neuromorphic Computing
title_full_unstemmed Magnetic Elements for Neuromorphic Computing
title_short Magnetic Elements for Neuromorphic Computing
title_sort magnetic elements for neuromorphic computing
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7321415/
https://www.ncbi.nlm.nih.gov/pubmed/32486173
http://dx.doi.org/10.3390/molecules25112550
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