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Moving deep learning to the edge

2020-05Journal article Open access
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dc.contributor.authorVéstias, Mário
dc.contributor.authorDuarte, Rui Policarpo
dc.contributor.authorDe Sousa, Jose
dc.contributor.authorNeto, Horácio C
dc.date.accessioned2020-05-26T12:54:42Z
dc.date.available2020-05-26T12:54:42Z
dc.date.issued2020-05
dc.descriptionEste trabalho foi financiado pelo Concurso Anual para Projetos de Investigação, Desenvolvimento, Inovação e Criação Artística (IDI&CA) 2019 do Instituto Politécnico de Lisboa. Código de referência IPL/2019/inCNeuraINet_ISEL
dc.description.abstractDeep learning is now present in a wide range of services and applications, replacing and complementing other machine learning algorithms. Performing training and inference of deep neural networks using the cloud computing model is not viable for applications where low latency is required. Furthermore, the rapid proliferation of the Internet of Things will generate a large volume of data to be processed, which will soon overload the capacity of cloud servers. One solution is to process the data at the edge devices themselves, in order to alleviate cloud server workloads and improve latency. However, edge devices are less powerful than cloud servers, and many are subject to energy constraints. Hence, new resource and energy-oriented deep learning models are required, as well as new computing platforms. This paper reviews the main research directions for edge computing deep learning algorithms.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationVÉSTIAS, Mário P.; [et al] – Moving deep learning to the edge. Algorithms. ISSN 1999-4893. Vol. 13, N.º 5 (2020), pp. 1-33pt_PT
dc.identifier.doi10.3390/a13050125pt_PT
dc.identifier.issn1999-4893
dc.identifier.urihttp://hdl.handle.net/10400.21/11717
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_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/2019/inCNeuraINet_ISELpt_PT
dc.relationUIDB/50021/2020 - FCTpt_PT
dc.subjectArtificial intelligencept_PT
dc.subjectDeep learningpt_PT
dc.subjectDeep neural networkpt_PT
dc.subjectEdge computingpt_PT
dc.titleMoving deep learning to the edgept_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage33pt_PT
oaire.citation.issue5pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleAlgorithmspt_PT
oaire.citation.volume13pt_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.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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relation.isAuthorOfPublication.latestForDiscoverya7d22b29-c961-45ac-bc09-cd5e1002f1e8

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