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CGPA: Coarse-Grained Pruning of Activations for Energy-Efficient RNN Inference
dc.contributor.author | Riera Villanueva, Marc |
dc.contributor.author | Arnau Montañés, José María |
dc.contributor.author | González Colás, Antonio María |
dc.contributor.other | Universitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors |
dc.date.accessioned | 2019-11-06T17:50:49Z |
dc.date.available | 2019-11-06T17:50:49Z |
dc.date.issued | 2019-09-01 |
dc.identifier.citation | Riera, 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.issn | 0272-1732 |
dc.identifier.uri | http://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.abstract | Recurrent 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.extent | 10 p. |
dc.language.iso | eng |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
dc.subject.lcsh | Machine learning |
dc.subject.other | Machine learning |
dc.subject.other | RNN |
dc.subject.other | Accelerators |
dc.subject.other | Low energy |
dc.title | CGPA: Coarse-Grained Pruning of Activations for Energy-Efficient RNN Inference |
dc.type | Article |
dc.subject.lemac | Aprenentatge automàtic |
dc.contributor.group | Universitat Politècnica de Catalunya. ARCO - Microarquitectura i Compiladors |
dc.identifier.doi | 10.1109/MM.2019.2929742 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://ieeexplore.ieee.org/document/8771118 |
dc.rights.access | Open Access |
local.identifier.drac | 25844865 |
dc.description.version | Postprint (author's final draft) |
dc.relation.projectid | info:eu-repo/grantAgreement/EC/H2020/833057/EU/CoCoUnit: An Energy-Efficient Processing Unit for Cognitive Computing/CoCoUnit |
dc.relation.projectid | info:eu-repo/grantAgreement/MINECO/1PE/TIN2016-75344-R |
local.citation.author | Riera, M.; Arnau, J.; Gonzalez, A. |
local.citation.publicationName | IEEE micro |
local.citation.volume | 39 |
local.citation.number | 5 |
local.citation.startingPage | 36 |
local.citation.endingPage | 45 |
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