Reconfigurable Neuromorphic Computing: Materials, Devices, and Integration

Neuromorphic computing has been attracting ever‐increasing attention due to superior energy efficiency, with great promise to promote the next wave of artificial general intelligence in the post‐Moore era. Current approaches are, however, broadly designed for stationary and unitary assignments, thus...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Advanced materials (Weinheim) 2023-12, Vol.35 (51), p.e2301063-n/a
Hauptverfasser: Xu, Minyi, Chen, Xinrui, Guo, Yehao, Wang, Yang, Qiu, Dong, Du, Xinchuan, Cui, Yi, Wang, Xianfu, Xiong, Jie
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page n/a
container_issue 51
container_start_page e2301063
container_title Advanced materials (Weinheim)
container_volume 35
creator Xu, Minyi
Chen, Xinrui
Guo, Yehao
Wang, Yang
Qiu, Dong
Du, Xinchuan
Cui, Yi
Wang, Xianfu
Xiong, Jie
description Neuromorphic computing has been attracting ever‐increasing attention due to superior energy efficiency, with great promise to promote the next wave of artificial general intelligence in the post‐Moore era. Current approaches are, however, broadly designed for stationary and unitary assignments, thus encountering reluctant interconnections, power consumption, and data‐intensive computing in that domain. Reconfigurable neuromorphic computing, an on‐demand paradigm inspired by the inherent programmability of brain, can maximally reallocate finite resources to perform the proliferation of reproducibly brain‐inspired functions, highlighting a disruptive framework for bridging the gap between different primitives. Although relevant research has flourished in diverse materials and devices with novel mechanisms and architectures, a precise overview remains blank and urgently desirable. Herein, the recent strides along this pursuit are systematically reviewed from material, device, and integration perspectives. At the material and device level, one comprehensively conclude the dominant mechanisms for reconfigurability, categorized into ion migration, carrier migration, phase transition, spintronics, and photonics. Integration‐level developments for reconfigurable neuromorphic computing are also exhibited. Finally, a perspective on the future challenges for reconfigurable neuromorphic computing is discussed, definitely expanding its horizon for scientific communities. Reconfigurable neuromorphic computing strives for bridging the gap between discrete intelligent paradigms. Developments about reconfigurable neuromorphic computing are systematically overviewed, in which the state‐of‐art strides from the material, device, and integration aspects are included, highlighting their significance in motivating the prosperity of general‐purposed intelligent computing. Perspective on the trends and obstacles of reconfigurable neuromorphic computing is also outlined.
doi_str_mv 10.1002/adma.202301063
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2823989810</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2823989810</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3733-66ff82bc6fe193fc9e9ef5b23e95b145af5ca754e9cae3ca120b48e20e0d013d3</originalsourceid><addsrcrecordid>eNqFkEtLxDAQgIMouj6uHqXgxYNdJ0nTTbwt6xsfIHoOaTpZI22zpq3iv7eyPsCLp5nDNx_DR8guhTEFYEemrM2YAeNAIecrZEQFo2kGSqySESguUpVncoNstu0zAKgc8nWywSdMCqHYiFzdow2N8_M-mqLC5Bb7GOoQF0_eJrNQL_rON_Pj5MZ0GL2p2sPkBF-9xWExTZlcNh3Oo-l8aLbJmhsA3PmaW-Tx7PRhdpFe351fzqbXqeUTztM8d06ywuYOqeLOKlToRME4KlHQTBgnrJmIDJU1yK2hDIpMIgOEEigv-RY5WHoXMbz02Ha69q3FqjINhr7VTDKupJIUBnT_D_oc-tgM32mmIAMp1SQbqPGSsjG0bUSnF9HXJr5rCvqzsv6srH8qDwd7X9q-qLH8wb-zDoBaAm--wvd_dHp6cjP9lX8ArqyIpQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2904088974</pqid></control><display><type>article</type><title>Reconfigurable Neuromorphic Computing: Materials, Devices, and Integration</title><source>Wiley Online Library Journals Frontfile Complete</source><creator>Xu, Minyi ; Chen, Xinrui ; Guo, Yehao ; Wang, Yang ; Qiu, Dong ; Du, Xinchuan ; Cui, Yi ; Wang, Xianfu ; Xiong, Jie</creator><creatorcontrib>Xu, Minyi ; Chen, Xinrui ; Guo, Yehao ; Wang, Yang ; Qiu, Dong ; Du, Xinchuan ; Cui, Yi ; Wang, Xianfu ; Xiong, Jie</creatorcontrib><description>Neuromorphic computing has been attracting ever‐increasing attention due to superior energy efficiency, with great promise to promote the next wave of artificial general intelligence in the post‐Moore era. Current approaches are, however, broadly designed for stationary and unitary assignments, thus encountering reluctant interconnections, power consumption, and data‐intensive computing in that domain. Reconfigurable neuromorphic computing, an on‐demand paradigm inspired by the inherent programmability of brain, can maximally reallocate finite resources to perform the proliferation of reproducibly brain‐inspired functions, highlighting a disruptive framework for bridging the gap between different primitives. Although relevant research has flourished in diverse materials and devices with novel mechanisms and architectures, a precise overview remains blank and urgently desirable. Herein, the recent strides along this pursuit are systematically reviewed from material, device, and integration perspectives. At the material and device level, one comprehensively conclude the dominant mechanisms for reconfigurability, categorized into ion migration, carrier migration, phase transition, spintronics, and photonics. Integration‐level developments for reconfigurable neuromorphic computing are also exhibited. Finally, a perspective on the future challenges for reconfigurable neuromorphic computing is discussed, definitely expanding its horizon for scientific communities. Reconfigurable neuromorphic computing strives for bridging the gap between discrete intelligent paradigms. Developments about reconfigurable neuromorphic computing are systematically overviewed, in which the state‐of‐art strides from the material, device, and integration aspects are included, highlighting their significance in motivating the prosperity of general‐purposed intelligent computing. Perspective on the trends and obstacles of reconfigurable neuromorphic computing is also outlined.</description><identifier>ISSN: 0935-9648</identifier><identifier>EISSN: 1521-4095</identifier><identifier>DOI: 10.1002/adma.202301063</identifier><identifier>PMID: 37285592</identifier><language>eng</language><publisher>Germany: Wiley Subscription Services, Inc</publisher><subject>Artificial intelligence ; Brain ; Ion migration ; Materials science ; multifunctional devices ; Neuromorphic computing ; Phase transitions ; Power consumption ; programmable devices ; reconfigurability ; reconfigurable integration ; Reconfiguration ; Spintronics</subject><ispartof>Advanced materials (Weinheim), 2023-12, Vol.35 (51), p.e2301063-n/a</ispartof><rights>2023 Wiley‐VCH GmbH</rights><rights>2023 Wiley-VCH GmbH.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3733-66ff82bc6fe193fc9e9ef5b23e95b145af5ca754e9cae3ca120b48e20e0d013d3</citedby><cites>FETCH-LOGICAL-c3733-66ff82bc6fe193fc9e9ef5b23e95b145af5ca754e9cae3ca120b48e20e0d013d3</cites><orcidid>0000-0003-3881-6948 ; 0000-0003-0924-5904 ; 0000-0002-2066-7473 ; 0000-0001-8449-915X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fadma.202301063$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fadma.202301063$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,27923,27924,45573,45574</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37285592$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Xu, Minyi</creatorcontrib><creatorcontrib>Chen, Xinrui</creatorcontrib><creatorcontrib>Guo, Yehao</creatorcontrib><creatorcontrib>Wang, Yang</creatorcontrib><creatorcontrib>Qiu, Dong</creatorcontrib><creatorcontrib>Du, Xinchuan</creatorcontrib><creatorcontrib>Cui, Yi</creatorcontrib><creatorcontrib>Wang, Xianfu</creatorcontrib><creatorcontrib>Xiong, Jie</creatorcontrib><title>Reconfigurable Neuromorphic Computing: Materials, Devices, and Integration</title><title>Advanced materials (Weinheim)</title><addtitle>Adv Mater</addtitle><description>Neuromorphic computing has been attracting ever‐increasing attention due to superior energy efficiency, with great promise to promote the next wave of artificial general intelligence in the post‐Moore era. Current approaches are, however, broadly designed for stationary and unitary assignments, thus encountering reluctant interconnections, power consumption, and data‐intensive computing in that domain. Reconfigurable neuromorphic computing, an on‐demand paradigm inspired by the inherent programmability of brain, can maximally reallocate finite resources to perform the proliferation of reproducibly brain‐inspired functions, highlighting a disruptive framework for bridging the gap between different primitives. Although relevant research has flourished in diverse materials and devices with novel mechanisms and architectures, a precise overview remains blank and urgently desirable. Herein, the recent strides along this pursuit are systematically reviewed from material, device, and integration perspectives. At the material and device level, one comprehensively conclude the dominant mechanisms for reconfigurability, categorized into ion migration, carrier migration, phase transition, spintronics, and photonics. Integration‐level developments for reconfigurable neuromorphic computing are also exhibited. Finally, a perspective on the future challenges for reconfigurable neuromorphic computing is discussed, definitely expanding its horizon for scientific communities. Reconfigurable neuromorphic computing strives for bridging the gap between discrete intelligent paradigms. Developments about reconfigurable neuromorphic computing are systematically overviewed, in which the state‐of‐art strides from the material, device, and integration aspects are included, highlighting their significance in motivating the prosperity of general‐purposed intelligent computing. Perspective on the trends and obstacles of reconfigurable neuromorphic computing is also outlined.</description><subject>Artificial intelligence</subject><subject>Brain</subject><subject>Ion migration</subject><subject>Materials science</subject><subject>multifunctional devices</subject><subject>Neuromorphic computing</subject><subject>Phase transitions</subject><subject>Power consumption</subject><subject>programmable devices</subject><subject>reconfigurability</subject><subject>reconfigurable integration</subject><subject>Reconfiguration</subject><subject>Spintronics</subject><issn>0935-9648</issn><issn>1521-4095</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNqFkEtLxDAQgIMouj6uHqXgxYNdJ0nTTbwt6xsfIHoOaTpZI22zpq3iv7eyPsCLp5nDNx_DR8guhTEFYEemrM2YAeNAIecrZEQFo2kGSqySESguUpVncoNstu0zAKgc8nWywSdMCqHYiFzdow2N8_M-mqLC5Bb7GOoQF0_eJrNQL_rON_Pj5MZ0GL2p2sPkBF-9xWExTZlcNh3Oo-l8aLbJmhsA3PmaW-Tx7PRhdpFe351fzqbXqeUTztM8d06ywuYOqeLOKlToRME4KlHQTBgnrJmIDJU1yK2hDIpMIgOEEigv-RY5WHoXMbz02Ha69q3FqjINhr7VTDKupJIUBnT_D_oc-tgM32mmIAMp1SQbqPGSsjG0bUSnF9HXJr5rCvqzsv6srH8qDwd7X9q-qLH8wb-zDoBaAm--wvd_dHp6cjP9lX8ArqyIpQ</recordid><startdate>20231201</startdate><enddate>20231201</enddate><creator>Xu, Minyi</creator><creator>Chen, Xinrui</creator><creator>Guo, Yehao</creator><creator>Wang, Yang</creator><creator>Qiu, Dong</creator><creator>Du, Xinchuan</creator><creator>Cui, Yi</creator><creator>Wang, Xianfu</creator><creator>Xiong, Jie</creator><general>Wiley Subscription Services, Inc</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-3881-6948</orcidid><orcidid>https://orcid.org/0000-0003-0924-5904</orcidid><orcidid>https://orcid.org/0000-0002-2066-7473</orcidid><orcidid>https://orcid.org/0000-0001-8449-915X</orcidid></search><sort><creationdate>20231201</creationdate><title>Reconfigurable Neuromorphic Computing: Materials, Devices, and Integration</title><author>Xu, Minyi ; Chen, Xinrui ; Guo, Yehao ; Wang, Yang ; Qiu, Dong ; Du, Xinchuan ; Cui, Yi ; Wang, Xianfu ; Xiong, Jie</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3733-66ff82bc6fe193fc9e9ef5b23e95b145af5ca754e9cae3ca120b48e20e0d013d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Artificial intelligence</topic><topic>Brain</topic><topic>Ion migration</topic><topic>Materials science</topic><topic>multifunctional devices</topic><topic>Neuromorphic computing</topic><topic>Phase transitions</topic><topic>Power consumption</topic><topic>programmable devices</topic><topic>reconfigurability</topic><topic>reconfigurable integration</topic><topic>Reconfiguration</topic><topic>Spintronics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Xu, Minyi</creatorcontrib><creatorcontrib>Chen, Xinrui</creatorcontrib><creatorcontrib>Guo, Yehao</creatorcontrib><creatorcontrib>Wang, Yang</creatorcontrib><creatorcontrib>Qiu, Dong</creatorcontrib><creatorcontrib>Du, Xinchuan</creatorcontrib><creatorcontrib>Cui, Yi</creatorcontrib><creatorcontrib>Wang, Xianfu</creatorcontrib><creatorcontrib>Xiong, Jie</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>MEDLINE - Academic</collection><jtitle>Advanced materials (Weinheim)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Xu, Minyi</au><au>Chen, Xinrui</au><au>Guo, Yehao</au><au>Wang, Yang</au><au>Qiu, Dong</au><au>Du, Xinchuan</au><au>Cui, Yi</au><au>Wang, Xianfu</au><au>Xiong, Jie</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Reconfigurable Neuromorphic Computing: Materials, Devices, and Integration</atitle><jtitle>Advanced materials (Weinheim)</jtitle><addtitle>Adv Mater</addtitle><date>2023-12-01</date><risdate>2023</risdate><volume>35</volume><issue>51</issue><spage>e2301063</spage><epage>n/a</epage><pages>e2301063-n/a</pages><issn>0935-9648</issn><eissn>1521-4095</eissn><abstract>Neuromorphic computing has been attracting ever‐increasing attention due to superior energy efficiency, with great promise to promote the next wave of artificial general intelligence in the post‐Moore era. Current approaches are, however, broadly designed for stationary and unitary assignments, thus encountering reluctant interconnections, power consumption, and data‐intensive computing in that domain. Reconfigurable neuromorphic computing, an on‐demand paradigm inspired by the inherent programmability of brain, can maximally reallocate finite resources to perform the proliferation of reproducibly brain‐inspired functions, highlighting a disruptive framework for bridging the gap between different primitives. Although relevant research has flourished in diverse materials and devices with novel mechanisms and architectures, a precise overview remains blank and urgently desirable. Herein, the recent strides along this pursuit are systematically reviewed from material, device, and integration perspectives. At the material and device level, one comprehensively conclude the dominant mechanisms for reconfigurability, categorized into ion migration, carrier migration, phase transition, spintronics, and photonics. Integration‐level developments for reconfigurable neuromorphic computing are also exhibited. Finally, a perspective on the future challenges for reconfigurable neuromorphic computing is discussed, definitely expanding its horizon for scientific communities. Reconfigurable neuromorphic computing strives for bridging the gap between discrete intelligent paradigms. Developments about reconfigurable neuromorphic computing are systematically overviewed, in which the state‐of‐art strides from the material, device, and integration aspects are included, highlighting their significance in motivating the prosperity of general‐purposed intelligent computing. Perspective on the trends and obstacles of reconfigurable neuromorphic computing is also outlined.</abstract><cop>Germany</cop><pub>Wiley Subscription Services, Inc</pub><pmid>37285592</pmid><doi>10.1002/adma.202301063</doi><tpages>39</tpages><orcidid>https://orcid.org/0000-0003-3881-6948</orcidid><orcidid>https://orcid.org/0000-0003-0924-5904</orcidid><orcidid>https://orcid.org/0000-0002-2066-7473</orcidid><orcidid>https://orcid.org/0000-0001-8449-915X</orcidid></addata></record>
fulltext fulltext
identifier ISSN: 0935-9648
ispartof Advanced materials (Weinheim), 2023-12, Vol.35 (51), p.e2301063-n/a
issn 0935-9648
1521-4095
language eng
recordid cdi_proquest_miscellaneous_2823989810
source Wiley Online Library Journals Frontfile Complete
subjects Artificial intelligence
Brain
Ion migration
Materials science
multifunctional devices
Neuromorphic computing
Phase transitions
Power consumption
programmable devices
reconfigurability
reconfigurable integration
Reconfiguration
Spintronics
title Reconfigurable Neuromorphic Computing: Materials, Devices, and Integration
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-09T03%3A54%3A32IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Reconfigurable%20Neuromorphic%20Computing:%20Materials,%20Devices,%20and%20Integration&rft.jtitle=Advanced%20materials%20(Weinheim)&rft.au=Xu,%20Minyi&rft.date=2023-12-01&rft.volume=35&rft.issue=51&rft.spage=e2301063&rft.epage=n/a&rft.pages=e2301063-n/a&rft.issn=0935-9648&rft.eissn=1521-4095&rft_id=info:doi/10.1002/adma.202301063&rft_dat=%3Cproquest_cross%3E2823989810%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2904088974&rft_id=info:pmid/37285592&rfr_iscdi=true