A Heterogenous and Reconfigurable Embedded Architecture for Energy-Efficient Execution of Convolutional Neural Networks

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dc.contributor.author Lübeck, Konstantin
dc.contributor.author Bringmann, Oliver
dc.date.accessioned 2020-05-25T14:07:09Z
dc.date.available 2020-05-25T14:07:09Z
dc.date.issued 2019
dc.identifier.uri http://hdl.handle.net/10900/100908
dc.language.iso en de_DE
dc.publisher Cham: Springer de_DE
dc.relation.ispartofseries Lecture notes in computer science;11479
dc.relation.uri http://dx.doi.org/10.1007/978-3-030-18656-2_20 de_DE
dc.subject.ddc 004 de_DE
dc.title A Heterogenous and Reconfigurable Embedded Architecture for Energy-Efficient Execution of Convolutional Neural Networks de_DE
dc.type Buch (einzelnes Kapitel) de_DE
dc.type Konferenzveröffentlichung de_DE
utue.publikation.seiten 267-280 de_DE
utue.personen.roh Lübeck, Konstantin
utue.personen.roh Bringmann, Oliver
utue.publikation.buchdesbeitrags Schoeberl M., Hochberger C., Uhrig S., Brehm J., Pionteck T. (eds) Architecture of Computing Systems – ARCS 2019 de_DE


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