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2022 roadmap on neuromorphic computing and engineering

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cam.issuedOnline2022-05-20
dc.contributor.authorChristensen, Dennis V
dc.contributor.authorDittmann, Regina
dc.contributor.authorLinares-Barranco, Bernabe
dc.contributor.authorSebastian, Abu
dc.contributor.authorLe Gallo, Manuel
dc.contributor.authorRedaelli, Andrea
dc.contributor.authorSlesazeck, Stefan
dc.contributor.authorMikolajick, Thomas
dc.contributor.authorSpiga, Sabina
dc.contributor.authorMenzel, Stephan
dc.contributor.authorValov, Ilia
dc.contributor.authorMilano, Gianluca
dc.contributor.authorRicciardi, Carlo
dc.contributor.authorLiang, Shi-Jun
dc.contributor.authorMiao, Feng
dc.contributor.authorLanza, Mario
dc.contributor.authorQuill, Tyler J
dc.contributor.authorKeene, Scott
dc.contributor.authorSalleo, Alberto
dc.contributor.authorGrollier, Julie
dc.contributor.authorMarković, Danijela
dc.contributor.authorMizrahi, Alice
dc.contributor.authorYao, Peng
dc.contributor.authorYang, J Joshua
dc.contributor.authorIndiveri, Giacomo
dc.contributor.authorStrachan, John Paul
dc.contributor.authorDatta, Suman
dc.contributor.authorVianello, Elisa
dc.contributor.authorValentian, Alexandre
dc.contributor.authorFeldmann, Johannes
dc.contributor.authorLi, Xuan
dc.contributor.authorPernice, Wolfram HP
dc.contributor.authorBhaskaran, Harish
dc.contributor.authorFurber, Steve
dc.contributor.authorNeftci, Emre
dc.contributor.authorScherr, Franz
dc.contributor.authorMaass, Wolfgang
dc.contributor.authorRamaswamy, Srikanth
dc.contributor.authorTapson, Jonathan
dc.contributor.authorPanda, Priyadarshini
dc.contributor.authorKim, Youngeun
dc.contributor.authorTanaka, Gouhei
dc.contributor.authorThorpe, Simon
dc.contributor.authorBartolozzi, Chiara
dc.contributor.authorCleland, Thomas A
dc.contributor.authorPosch, Christoph
dc.contributor.authorLiu, ShihChii
dc.contributor.authorPanuccio, Gabriella
dc.contributor.authorMahmud, Mufti
dc.contributor.authorMazumder, Arnab Neelim
dc.contributor.authorHosseini, Morteza
dc.contributor.authorMohsenin, Tinoosh
dc.contributor.authorDonati, Elisa
dc.contributor.authorTolu, Silvia
dc.contributor.authorGaleazzi, Roberto
dc.contributor.authorChristensen, Martin Ejsing
dc.contributor.authorHolm, Sune
dc.contributor.authorIelmini, Daniele
dc.contributor.authorPryds, N
dc.contributor.orcidChristensen, Dennis V [0000-0003-0048-7595]
dc.contributor.orcidSebastian, Abu [0000-0001-5603-5243]
dc.contributor.orcidLe Gallo, Manuel [0000-0003-1600-6151]
dc.contributor.orcidSlesazeck, Stefan [0000-0002-0414-0321]
dc.contributor.orcidMikolajick, Thomas [0000-0003-3814-0378]
dc.contributor.orcidSpiga, Sabina [0000-0001-7293-7503]
dc.contributor.orcidValov, Ilia [0000-0002-0728-7214]
dc.contributor.orcidMilano, Gianluca [0000-0002-1983-6516]
dc.contributor.orcidRicciardi, Carlo [0000-0002-4703-7949]
dc.contributor.orcidLiang, Shi-Jun [0000-0002-3466-8063]
dc.contributor.orcidMiao, Feng [0000-0002-0962-5424]
dc.contributor.orcidLanza, Mario [0000-0003-4756-8632]
dc.contributor.orcidKeene, Scott [0000-0002-6635-670X]
dc.contributor.orcidMizrahi, Alice [0000-0003-2043-049X]
dc.contributor.orcidYang, J Joshua [0000-0001-8242-7531]
dc.contributor.orcidIndiveri, Giacomo [0000-0002-7109-1689]
dc.contributor.orcidStrachan, John Paul [0000-0002-5718-7924]
dc.contributor.orcidVianello, Elisa [0000-0002-8868-9951]
dc.contributor.orcidRamaswamy, Srikanth [0000-0001-6642-7136]
dc.contributor.orcidPanuccio, Gabriella [0000-0002-4059-7193]
dc.contributor.orcidMahmud, Mufti [0000-0002-2037-8348]
dc.contributor.orcidDonati, Elisa [0000-0002-8091-1298]
dc.contributor.orcidTolu, Silvia [0000-0003-1825-8440]
dc.contributor.orcidIelmini, Daniele [0000-0002-1853-1614]
dc.contributor.orcidPryds, N [0000-0002-5718-7924]
dc.date.accessioned2022-06-07T08:10:13Z
dc.date.available2022-06-07T08:10:13Z
dc.date.issued2022-06-01
dc.date.submitted2021-05-14
dc.date.updated2022-06-07T08:10:13Z
dc.descriptionFunder: ETH Zürich Foundation; doi: https://doi.org/10.13039/501100012652
dc.descriptionFunder: Defense Advanced Research Projects Agency; doi: https://doi.org/10.13039/100000185
dc.descriptionFunder: Collaborative Innovation Center of Advanced Microstructures; doi: https://doi.org/10.13039/501100016018
dc.descriptionFunder: Bundesministerium für Bildung und Forschung; doi: https://doi.org/10.13039/501100002347
dc.descriptionFunder: Centre National de la Recherche Scientifique; doi: https://doi.org/10.13039/501100004794
dc.descriptionFunder: Freistaat Sachsen; doi: https://doi.org/10.13039/501100014913
dc.descriptionFunder: Deutsche Forschungsgemeinschaft; doi: https://doi.org/10.13039/501100001659
dc.descriptionFunder: National Institute on Deafness and Other Communication Disorders; doi: https://doi.org/10.13039/100000055
dc.description.abstract<jats:title>Abstract</jats:title> <jats:p>Modern computation based on von Neumann architecture is now a mature cutting-edge science. In the von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 10<jats:sup>18</jats:sup> calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community.</jats:p>
dc.identifier.doi10.17863/CAM.85140
dc.identifier.eissn2634-4386
dc.identifier.issn2634-4386
dc.identifier.othernceac4a83
dc.identifier.otherac4a83
dc.identifier.othernce-100024.r1
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/337731
dc.languageen
dc.publisherIOP Publishing
dc.subjectRoadmap
dc.subjectneuromorphic computation
dc.subjectspiking neural networks
dc.subjectrobotics
dc.subjectmemristor
dc.subjectconvolutional neural networks
dc.subjectself-driving cars
dc.subjectdeep learning
dc.title2022 roadmap on neuromorphic computing and engineering
dc.typeArticle
dcterms.dateAccepted2022-01-12
prism.issueIdentifier2
prism.publicationNameNeuromorphic Computing and Engineering
prism.volume2
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/M015130/1)
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/
rioxxterms.versionVoR
rioxxterms.versionofrecord10.1088/2634-4386/ac4a83

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