VEDLIoT: Very Efficient Deep Learning in IoT

DC ElementWertSprache
dc.contributor.authorKaiser, M.
dc.contributor.authorGriessl, R.
dc.contributor.authorKucza, N.
dc.contributor.authorHaumann, C.
dc.contributor.authorTigges, L.
dc.contributor.authorMika, K.
dc.contributor.authorHagemeyer, J.
dc.contributor.authorPorrmann, F.
dc.contributor.authorRuckert, U.
dc.contributor.authorVor Dem Berge, M.
dc.contributor.authorKrupop, S.
dc.contributor.authorPorrmann, M.
dc.contributor.authorTassemeier, M.
dc.contributor.authorTrancoso, P.
dc.contributor.authorQararyah, F.
dc.contributor.authorZouzoula, S.
dc.contributor.authorCasimiro, A.
dc.contributor.authorBessani, A.
dc.contributor.authorCecilio, J.
dc.contributor.authorAndersson, S.
dc.contributor.authorBrunnegard, O.
dc.contributor.authorEriksson, O.
dc.contributor.authorWeiss, R.
dc.contributor.authorMcIerhofer, F.
dc.contributor.authorSalomonsson, H.
dc.contributor.authorMalekzadeh, E.
dc.contributor.authorOdman, D.
dc.contributor.authorKhurshid, A.
dc.contributor.authorFelber, P.
dc.contributor.authorPasin, M.
dc.contributor.authorSchiavoni, V.
dc.contributor.authorMenetrey, J.
dc.contributor.authorGugala, K.
dc.contributor.authorZierhoffer, P.
dc.contributor.authorKnauss, E.
dc.contributor.authorHeyn, H.
dc.contributor.editorBolchini, C.
dc.contributor.editorVerbauwhede, I.
dc.contributor.editorVatajelu, I.
dc.date.accessioned2023-02-17T12:15:25Z-
dc.date.available2023-02-17T12:15:25Z-
dc.date.issued2022
dc.identifier.isbn9783981926361
dc.identifier.urihttp://osnascholar.ub.uni-osnabrueck.de/handle/unios/65956-
dc.descriptionConference of 2022 Design, Automation and Test in Europe Conference and Exhibition, DATE 2022 ; Conference Date: 14 March 2022 Through 23 March 2022; Conference Code:179397
dc.description.abstractThe VEDLIoT project targets the development of energy-efficient Deep Learning for distributed AIoT applications. A holistic approach is used to optimize algorithms while also dealing with safety and security challenges. The approach is based on a modular and scalable cognitive IoT hardware platform. Using modular microserver technology enables the user to configure the hardware to satisfy a wide range of applications. VEDLIoT offers a complete design flow for Next-Generation IoT devices required for collaboratively solving complex Deep Learning applications across distributed systems. The methods are tested on various use-cases ranging from Smart Home to Automotive and Industrial IoT appliances. VEDLIoT is an H2020 EU project which started in November 2020. It is currently in an intermediate stage with the first results available. © 2022 EDAA.
dc.description.sponsorshipHorizon 2020 Framework ProgrammeHorizon 2020 Framework Programme,H2020,957197; This publication incorporates results from the VEDLIoT project, which received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 957197.; Cadence; CEA; et al.; HIPEAC; IEEE Council on Electronic Design Automation (CEDA); NanoElec
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofProceedings of the 2022 Design, Automation and Test in Europe Conference and Exhibition, DATE 2022
dc.subjectAutomation
dc.subjectDeep learning
dc.subjectEnergy efficiency, Automotives
dc.subjectDesign flows
dc.subjectEnergy efficient
dc.subjectHardware platform
dc.subjectHolistic approach
dc.subjectMicro-servers
dc.subjectModulars
dc.subjectSafety and securities
dc.subjectSecurity challenges
dc.subjectSmart homes, Internet of things
dc.titleVEDLIoT: Very Efficient Deep Learning in IoT
dc.typeconference paper
dc.identifier.doi10.23919/DATE54114.2022.9774653
dc.identifier.scopus2-s2.0-85130802370
dc.identifier.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85130802370&doi=10.23919%2fDATE54114.2022.9774653&partnerID=40&md5=c8e222c059ea5f49deaa78c5cfb271c6
dc.description.startpage963
dc.description.endpage968
dcterms.isPartOf.abbreviationProc. Des., Autom. Test Europe Conf. Exhib., DATE
crisitem.author.deptFB 06 - Mathematik/Informatik-
crisitem.author.deptidfb06-
crisitem.author.orcid0000-0003-1005-5753-
crisitem.author.parentorgUniversität Osnabrück-
crisitem.author.netidPoMa309-
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