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Lite-CNN: a high-performance architecture to execute CNNs in low density FPGAs

2018-08Conference object Open access
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dc.contributor.authorVéstias, Mário
dc.contributor.authorDuarte, Rui
dc.contributor.authorDe Sousa, Jose
dc.contributor.authorCláudio de Campos Neto, Horácio
dc.date.accessioned2018-10-10T09:20:54Z
dc.date.available2018-10-10T09:20:54Z
dc.date.issued2018-08
dc.description.abstractDue to the computational complexity of Convolutional Neural Networks (CNNs), high performance platforms are generally considered for their execution. However, CNNs are very useful in embedded systems and its execution right next to the source of data has many advantages, like avoiding the need for data communication. In this paper, we propose an architecture for CNN inference (Lite-CNN) that can achieve high performance in low density FPGAs. Lite-CNN adopts a fixed-point representation for both neurons and weights, which was already shown to be sufficient for most CNNs. Also, with a simple and known dot product reorganization, the number of multiplications is reduced to half. We show implementation results for 8 bit fixed-point in a ZYNQ7020 and extrapolate for other larger FPGAs. Lite-CNN achieves 410 GOPs in a ZYNQ7020.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationVÉSTIAS, Mário; [et al] – Lite-CNN: a high-performance architecture to execute CNNs in low density FPGAs. In 28th International Conference on Field Programmable Logic & Applications. Dublin, Ireland: 2018. Pp. 399-402pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.21/8903
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.subjectEmbedded computingpt_PT
dc.subjectDeep learningpt_PT
dc.subjectConvolutional neural networkpt_PT
dc.subjectField-programmable gate arraypt_PT
dc.titleLite-CNN: a high-performance architecture to execute CNNs in low density FPGAspt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/5876/UID%2FCEC%2F50021%2F2013/PT
oaire.citation.conferencePlace27-31 August 2018 - Dublin, Irelandpt_PT
oaire.citation.endPage402pt_PT
oaire.citation.startPage399pt_PT
oaire.citation.title28th International Conference on Field Programmable Logic & Applicationspt_PT
oaire.fundingStream5876
person.familyNameVéstias
person.familyNameDuarte
person.familyNamede Sousa
person.familyNameCláudio de Campos Neto
person.givenNameMário
person.givenNameRui
person.givenNameJose
person.givenNameHorácio
person.identifier.ciencia-id4717-C2C7-3F2C
person.identifier.ciencia-idB91E-770F-19A3
person.identifier.ciencia-idBE18-E262-E0EC
person.identifier.ciencia-id9915-3BDF-5C35
person.identifier.orcid0000-0001-8556-4507
person.identifier.orcid0000-0002-7060-4745
person.identifier.orcid0000-0001-7525-7546
person.identifier.orcid0000-0002-3621-8322
person.identifier.ridH-9953-2012
person.identifier.ridI-4402-2015
person.identifier.ridL-6859-2015
person.identifier.scopus-author-id14525867300
person.identifier.scopus-author-id24823991600
person.identifier.scopus-author-id7102813024
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsclosedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublicationa7d22b29-c961-45ac-bc09-cd5e1002f1e8
relation.isAuthorOfPublicationf2b4b9e6-6c89-48c7-bc83-62d2e98a787b
relation.isAuthorOfPublicationd98a4d45-2d45-42ec-9f1d-14775723709b
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relation.isAuthorOfPublication.latestForDiscovery38334d5e-83e8-494c-a9e0-396299376d97
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