Reservoir Computing with Planar Nanomagnet Arrays

Reservoir Computing with Planar Nanomagnet Arrays

Reservoir computing is an emerging methodology for neuromorphic computing that is especially well-suited for hardware implementations in size, weight, and power (SWaP) constrained environments. This work proposes a novel hardware implementation of a reservoir computer using a planar nanomagnet array...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:arXiv.org 2020-03
Hauptverfasser: Zhou, Peng, McDonald, Nathan R, Edwards, Alexander J, Loomis, Lisa, Thiem, Clare D, Friedman, Joseph S
Format: Artikel
Sprache:eng
Schlagworte:
Arrays
Computation
Computer simulation
Hardware
Waveforms
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title arXiv.org
container_volume
creator Zhou, Peng
McDonald, Nathan R
Edwards, Alexander J
Loomis, Lisa
Thiem, Clare D
Friedman, Joseph S
description Reservoir computing is an emerging methodology for neuromorphic computing that is especially well-suited for hardware implementations in size, weight, and power (SWaP) constrained environments. This work proposes a novel hardware implementation of a reservoir computer using a planar nanomagnet array. A small nanomagnet reservoir is demonstrated via micromagnetic simulations to be able to identify simple waveforms with 100% accuracy. Planar nanomagnet reservoirs are a promising new solution to the growing need for dedicated neuromorphic hardware.
format Article
fullrecord <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_2382890121</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2382890121</sourcerecordid><originalsourceid>FETCH-proquest_journals_23828901213</originalsourceid><addsrcrecordid>eNpjYuA0MjY21LUwMTLiYOAtLs4yMDAwMjM3MjU15mQwDEotTi0qy88sUnDOzy0oLcnMS1cozyzJUAjIScxLLFLwS8zLz01Mz0stUXAsKkqsLOZhYE1LzClO5YXS3AzKbq4hzh66BUX5haWpxSXxWfmlRXlAqXgjYwsjC0sDQyNDY-JUAQDCdzOp</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2382890121</pqid></control><display><type>article</type><title>Reservoir Computing with Planar Nanomagnet Arrays</title><source>Free E- Journals</source><creator>Zhou, Peng ; McDonald, Nathan R ; Edwards, Alexander J ; Loomis, Lisa ; Thiem, Clare D ; Friedman, Joseph S</creator><creatorcontrib>Zhou, Peng ; McDonald, Nathan R ; Edwards, Alexander J ; Loomis, Lisa ; Thiem, Clare D ; Friedman, Joseph S</creatorcontrib><description>Reservoir computing is an emerging methodology for neuromorphic computing that is especially well-suited for hardware implementations in size, weight, and power (SWaP) constrained environments. This work proposes a novel hardware implementation of a reservoir computer using a planar nanomagnet array. A small nanomagnet reservoir is demonstrated via micromagnetic simulations to be able to identify simple waveforms with 100% accuracy. Planar nanomagnet reservoirs are a promising new solution to the growing need for dedicated neuromorphic hardware.</description><identifier>EISSN: 2331-8422</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Arrays ; Computation ; Computer simulation ; Hardware ; Waveforms</subject><ispartof>arXiv.org, 2020-03</ispartof><rights>2020. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>780,784</link.rule.ids></links><search><creatorcontrib>Zhou, Peng</creatorcontrib><creatorcontrib>McDonald, Nathan R</creatorcontrib><creatorcontrib>Edwards, Alexander J</creatorcontrib><creatorcontrib>Loomis, Lisa</creatorcontrib><creatorcontrib>Thiem, Clare D</creatorcontrib><creatorcontrib>Friedman, Joseph S</creatorcontrib><title>Reservoir Computing with Planar Nanomagnet Arrays</title><title>arXiv.org</title><description>Reservoir computing is an emerging methodology for neuromorphic computing that is especially well-suited for hardware implementations in size, weight, and power (SWaP) constrained environments. This work proposes a novel hardware implementation of a reservoir computer using a planar nanomagnet array. A small nanomagnet reservoir is demonstrated via micromagnetic simulations to be able to identify simple waveforms with 100% accuracy. Planar nanomagnet reservoirs are a promising new solution to the growing need for dedicated neuromorphic hardware.</description><subject>Arrays</subject><subject>Computation</subject><subject>Computer simulation</subject><subject>Hardware</subject><subject>Waveforms</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpjYuA0MjY21LUwMTLiYOAtLs4yMDAwMjM3MjU15mQwDEotTi0qy88sUnDOzy0oLcnMS1cozyzJUAjIScxLLFLwS8zLz01Mz0stUXAsKkqsLOZhYE1LzClO5YXS3AzKbq4hzh66BUX5haWpxSXxWfmlRXlAqXgjYwsjC0sDQyNDY-JUAQDCdzOp</recordid><startdate>20200324</startdate><enddate>20200324</enddate><creator>Zhou, Peng</creator><creator>McDonald, Nathan R</creator><creator>Edwards, Alexander J</creator><creator>Loomis, Lisa</creator><creator>Thiem, Clare D</creator><creator>Friedman, Joseph S</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20200324</creationdate><title>Reservoir Computing with Planar Nanomagnet Arrays</title><author>Zhou, Peng ; McDonald, Nathan R ; Edwards, Alexander J ; Loomis, Lisa ; Thiem, Clare D ; Friedman, Joseph S</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_23828901213</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Arrays</topic><topic>Computation</topic><topic>Computer simulation</topic><topic>Hardware</topic><topic>Waveforms</topic><toplevel>online_resources</toplevel><creatorcontrib>Zhou, Peng</creatorcontrib><creatorcontrib>McDonald, Nathan R</creatorcontrib><creatorcontrib>Edwards, Alexander J</creatorcontrib><creatorcontrib>Loomis, Lisa</creatorcontrib><creatorcontrib>Thiem, Clare D</creatorcontrib><creatorcontrib>Friedman, Joseph S</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhou, Peng</au><au>McDonald, Nathan R</au><au>Edwards, Alexander J</au><au>Loomis, Lisa</au><au>Thiem, Clare D</au><au>Friedman, Joseph S</au><format>book</format><genre>document</genre><ristype>GEN</ristype><atitle>Reservoir Computing with Planar Nanomagnet Arrays</atitle><jtitle>arXiv.org</jtitle><date>2020-03-24</date><risdate>2020</risdate><eissn>2331-8422</eissn><abstract>Reservoir computing is an emerging methodology for neuromorphic computing that is especially well-suited for hardware implementations in size, weight, and power (SWaP) constrained environments. This work proposes a novel hardware implementation of a reservoir computer using a planar nanomagnet array. A small nanomagnet reservoir is demonstrated via micromagnetic simulations to be able to identify simple waveforms with 100% accuracy. Planar nanomagnet reservoirs are a promising new solution to the growing need for dedicated neuromorphic hardware.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier EISSN: 2331-8422
ispartof arXiv.org, 2020-03
issn 2331-8422
language eng
recordid cdi_proquest_journals_2382890121
source Free E- Journals
subjects Arrays
Computation
Computer simulation
Hardware
Waveforms
title Reservoir Computing with Planar Nanomagnet Arrays
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-18T22%3A08%3A12IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=document&rft.atitle=Reservoir%20Computing%20with%20Planar%20Nanomagnet%20Arrays&rft.jtitle=arXiv.org&rft.au=Zhou,%20Peng&rft.date=2020-03-24&rft.eissn=2331-8422&rft_id=info:doi/&rft_dat=%3Cproquest%3E2382890121%3C/proquest%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2382890121&rft_id=info:pmid/&rfr_iscdi=true