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dc.contributor.authorRiera Villanueva, Marc
dc.contributor.authorArnau Montañés, José María
dc.contributor.authorGonzález Colás, Antonio María
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors
dc.date.accessioned2019-11-06T17:50:49Z
dc.date.available2019-11-06T17:50:49Z
dc.date.issued2019-09-01
dc.identifier.citationRiera, M.; Arnau, J.; Gonzalez, A. CGPA: Coarse-Grained Pruning of Activations for Energy-Efficient RNN Inference. "IEEE micro", 1 Setembre 2019, vol. 39, núm. 5, p. 36-45.
dc.identifier.issn0272-1732
dc.identifier.urihttp://hdl.handle.net/2117/171871
dc.description© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.description.abstractRecurrent neural networks (RNNs) perform element-wise multiplications across the activations of gates. We show that a significant percentage of activations are saturated and propose coarse-grained pruning of activations (CGPA) to avoid the computation of entire neurons, based on the activation values of the gates. We show that CGPA can be easily implemented on top of a TPU-like architecture with negligible area overhead, resulting in 12% speedup and 12% energy savings on average for a set of widely used RNNs.
dc.format.extent10 p.
dc.language.isoeng
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
dc.subject.lcshMachine learning
dc.subject.otherMachine learning
dc.subject.otherRNN
dc.subject.otherAccelerators
dc.subject.otherLow energy
dc.titleCGPA: Coarse-Grained Pruning of Activations for Energy-Efficient RNN Inference
dc.typeArticle
dc.subject.lemacAprenentatge automàtic
dc.contributor.groupUniversitat Politècnica de Catalunya. ARCO - Microarquitectura i Compiladors
dc.identifier.doi10.1109/MM.2019.2929742
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/8771118
dc.rights.accessOpen Access
local.identifier.drac25844865
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/833057/EU/CoCoUnit: An Energy-Efficient Processing Unit for Cognitive Computing/CoCoUnit
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/1PE/TIN2016-75344-R
local.citation.authorRiera, M.; Arnau, J.; Gonzalez, A.
local.citation.publicationNameIEEE micro
local.citation.volume39
local.citation.number5
local.citation.startingPage36
local.citation.endingPage45


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