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Hybrid dot-product calculation for convolutional neural networks in FPGA

dc.contributor.authorVéstias, Mário
dc.contributor.authorDuarte, Rui Policarpo
dc.contributor.authorDe Sousa, Jose
dc.contributor.authorCláudio de Campos Neto, Horácio
dc.date.accessioned2019-11-19T11:52:07Z
dc.date.available2019-11-19T11:52:07Z
dc.date.issued2019-11-07
dc.descriptionEste trabalho foi financiado pelo Concurso Anual para Projetos de Investigação, Desenvolvimento, Inovação e Criação Artística (IDI&CA) 2018 do Instituto Politécnico de Lisboa. Código de referência IPL/2018/LiteCNN_ISEL
dc.description.abstractConvolutional Neural Networks (CNN) are quite useful in edge devices for security, surveillance, and many others. Running CNNs in embedded devices is a design challenge since these models require high computing power and large memory storage. Data quantization is an optimization technique applied to CNN to reduce the computing and memory requirements. The method reduces the number of bits used to represent weights and activations, which consequently reduces the size of operands and of the memory. The method is more effective if hybrid quantization is considered in which data in different layers may have different bit widths. This article proposes a new hardware module to calculate dot-products of CNNs with hybrid quantization. The module improves the implementation of CNNs in low density FPGAs, where the same module runs dot-products of different layers with different data quantizations. We show implementation results in ZYNQ7020 and compare with state-of-the-art works. Improvements in area and performance are achieved with the new proposed module.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationVÉSTIAS, Mário P.; [et al] – Hybrid dot-product calculation for convolutional neural networks in FPGA. In 2019 29th International Conference on Field Programmable Logic and Applications (FPL). Barcelona, Spain: IEEE, 2019. ISBN 978-1-7281-4884-7. Pp. 350-353pt_PT
dc.identifier.doi10.1109/FPL.2019.00062pt_PT
dc.identifier.isbn978-1-7281-4884-7
dc.identifier.isbn978-1-7281-4885-4
dc.identifier.issn1946-1488
dc.identifier.issn1946-147X
dc.identifier.urihttp://hdl.handle.net/10400.21/10716
dc.language.isoengpt_PT
dc.publisherInstitute of Electrical and Electronics Engineerspt_PT
dc.relationUID/CEC/50021/2019 - FCTpt_PT
dc.relationProjeto financiado no âmbito do Concurso de Projetos de Investigação, Desenvolvimento, Inovação & Criação Artística (IDI&CA) financiados pelo Instituto Politécnico de Lisboa. IPL/2018/LiteCNN_ISELpt_PT
dc.relation.publisherversionhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8892075pt_PT
dc.subjectDeep learningpt_PT
dc.subjectConvolutional Neural Networkpt_PT
dc.subjectEmbedded computingpt_PT
dc.subjectHybrid quantizationpt_PT
dc.subjectField-Programmable Gate Arraypt_PT
dc.titleHybrid dot-product calculation for convolutional neural networks in FPGApt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlace8-12 Sept. 2019 - Barcelona, Spainpt_PT
oaire.citation.endPage353pt_PT
oaire.citation.startPage350pt_PT
oaire.citation.title2019 29th International Conference on Field Programmable Logic and Applications (FPL)pt_PT
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
rcaap.rightsclosedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublicationa7d22b29-c961-45ac-bc09-cd5e1002f1e8
relation.isAuthorOfPublicationf2b4b9e6-6c89-48c7-bc83-62d2e98a787b
relation.isAuthorOfPublicationd98a4d45-2d45-42ec-9f1d-14775723709b
relation.isAuthorOfPublication38334d5e-83e8-494c-a9e0-396299376d97
relation.isAuthorOfPublication.latestForDiscoveryf2b4b9e6-6c89-48c7-bc83-62d2e98a787b

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