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dc.contributor.authorRajendran, Bipin
dc.contributor.authorSebastian, Abu
dc.contributor.authorSchmuker, Michael
dc.contributor.authorSrinivasa, Nayaran
dc.contributor.authorEleftheriou, Evangelos
dc.date.accessioned2019-11-08T01:18:41Z
dc.date.available2019-11-08T01:18:41Z
dc.date.issued2019-11-01
dc.identifier.citationRajendran , B , Sebastian , A , Schmuker , M , Srinivasa , N & Eleftheriou , E 2019 , ' Low-Power Neuromorphic Hardware for Signal Processing Applications : A review of architectural and system-level design approaches ' , IEEE Signal Processing Magazine , vol. 36 , no. 6 , 8888024 , pp. 97-110 . https://doi.org/10.1109/MSP.2019.2933719
dc.identifier.otherPURE: 16546142
dc.identifier.otherPURE UUID: 0a6c21dc-4447-45d2-96b5-9adb0799403a
dc.identifier.otherScopus: 85074460168
dc.identifier.urihttp://hdl.handle.net/2299/21865
dc.description© 2019 IEEE
dc.description.abstractMachine learning has emerged as the dominant tool for implementing complex cognitive tasks that require supervised, unsupervised, and reinforcement learning. While the resulting machines have demonstrated in some cases even superhuman performance, their energy consumption has often proved to be prohibitive in the absence of costly supercomputers. Most state-of-the-art machine-learning solutions are based on memoryless models of neurons. This is unlike the neurons in the human brain that encode and process information using temporal information in spike events. The different computing principles underlying biological neurons and how they combine together to efficiently process information is believed to be a key factor behind their superior efficiency compared to current machine-learning systems.en
dc.format.extent14
dc.language.isoeng
dc.relation.ispartofIEEE Signal Processing Magazine
dc.rightsEmbargoed
dc.subjectSignal Processing
dc.subjectElectrical and Electronic Engineering
dc.subjectApplied Mathematics
dc.titleLow-Power Neuromorphic Hardware for Signal Processing Applications : A review of architectural and system-level design approachesen
dc.contributor.institutionCentre of Data Innovation Research
dc.contributor.institutionSchool of Engineering and Computer Science
dc.contributor.institutionDepartment of Computer Science
dc.description.statusPeer reviewed
dc.date.embargoedUntil2020-11-01
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85074460168&partnerID=8YFLogxK
dc.identifier.urlhttps://arxiv.org/abs/1901.03690
dc.relation.schoolSchool of Engineering and Computer Science
dc.description.versiontypeFinal Accepted Version
dcterms.dateAccepted2019-11-01
rioxxterms.versionAM
rioxxterms.versionofrecordhttps://doi.org/10.1109/MSP.2019.2933719
rioxxterms.licenseref.uriOther
rioxxterms.licenseref.startdate2020-11-01
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue
herts.date.embargo2020-11-01
herts.rights.accesstypeEmbargoed


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