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dc.contributor.authorSteven, G.B.
dc.contributor.authorAnguera, R.
dc.contributor.authorEgan, C.
dc.contributor.authorSteven, F.L.
dc.contributor.authorVintan, L.
dc.date.accessioned2011-11-09T11:01:15Z
dc.date.available2011-11-09T11:01:15Z
dc.date.issued2001
dc.identifier.citationSteven , G B , Anguera , R , Egan , C , Steven , F L & Vintan , L 2001 , Dynamic branch prediction using neural networks . in Procs of Euromicro Symposium on Digital Systems, Design . vol. 2001 , IEEE , pp. 178-185 . https://doi.org/10.1109/DSD.2001.952279
dc.identifier.isbn0-7695-1239-9
dc.identifier.otherPURE: 446597
dc.identifier.otherPURE UUID: 5a6895e3-787e-4f6f-b5da-79a719f1748c
dc.identifier.otherdspace: 2299/1655
dc.identifier.otherScopus: 84969584440
dc.identifier.urihttp://hdl.handle.net/2299/6961
dc.description.abstractDynamic branch prediction in high-performance processors is a specific instance of a general time series prediction problem that occurs in many areas of science. In contrast, most branch prediction research focuses on two-level adaptive branch prediction techniques, a very specific solution to the branch prediction problem. An alternative approach is to look to other application areas and fields for novel solutions to the problem. In this paper, we examine the application of neural networks to dynamic branch prediction. Two neural networks are considered: a lecturing vector quantisation (LVQ) Network and a backpropagation network. We demonstrate that a neural predictor can achieve misprediction rates comparable to conventional two-level adaptive predictors and suggest that neural predictors merit further investigation.en
dc.language.isoeng
dc.publisherIEEE
dc.relation.ispartofProcs of Euromicro Symposium on Digital Systems, Design
dc.rightsOpen
dc.titleDynamic branch prediction using neural networksen
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionScience & Technology Research Institute
dc.description.versiontypeFinal Published version
dcterms.dateAccepted2001
rioxxterms.versionVoR
rioxxterms.versionofrecordhttps://doi.org/10.1109/DSD.2001.952279
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue
herts.rights.accesstypeOpen


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