PLAM: a posit logarithm-approximate multiplier

The Posit™ Number System was introduced in 2017 as a replacement for floating-point numbers. Since then, the community has explored its application in several areas, such as deep learning, and produced some unit designs which are still far from being competitive with their floating-point counterpart...

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Autores: Murillo Montero, Raúl, Del Barrio García, Alberto Antonio, Botella Juan, Guillermo, Kim, Min Soo, Kim, HyunJin, Bagherzadeh, Nader
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:inglés
OAI Identifier:oai:docta.ucm.es:20.500.14352/132729
Acceso en línea:https://hdl.handle.net/20.500.14352/132729
Access Level:acceso abierto
Palabra clave:004.3
621.38
Posit arithmetic
Arithmetic and logic structures
Low-power design
Machine learning
Computer vision
Informática (Informática)
Hardware
1203 Ciencia de Los Ordenadores
3307.03 Diseño de Circuitos
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oai_identifier_str oai:docta.ucm.es:20.500.14352/132729
network_acronym_str ES
network_name_str España
repository_id_str
spelling PLAM: a posit logarithm-approximate multiplierMurillo Montero, RaúlDel Barrio García, Alberto AntonioBotella Juan, GuillermoKim, Min SooKim, HyunJinBagherzadeh, Nader004.3621.38Posit arithmeticArithmetic and logic structuresLow-power designMachine learningComputer visionInformática (Informática)Hardware1203 Ciencia de Los Ordenadores3307.03 Diseño de CircuitosThe Posit™ Number System was introduced in 2017 as a replacement for floating-point numbers. Since then, the community has explored its application in several areas, such as deep learning, and produced some unit designs which are still far from being competitive with their floating-point counterparts. This article proposes a Posit Logarithm-Approximate Multiplication (PLAM) scheme to significantly reduce the complexity of posit multipliers, one of the most power-hungry arithmetic units. The impact of this approach is evaluated in deep neural network inference, where there are no significant accuracy drops. Compared with state-of-the-art posit multipliers, experiments show that the proposed technique reduces the area, power, and delay of 32-bit hardware multipliers up to 72.86%, 81.79%, and 17.01%, respectively.IEEEUniversidad Complutense de Madrid20212021-09-0620212021-09-06journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14352/132729reponame:Docta Complutenseinstname:Universidad Complutense de Madrid (UCM)InglésengAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 RTI2018-093684-B-I00 HETEROGENEIDAD Y ESPECIALIZACION EN LA ERA POST-MOOREopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:docta.ucm.es:20.500.14352/1327292026-06-02T12:44:21Z
dc.title.none.fl_str_mv PLAM: a posit logarithm-approximate multiplier
title PLAM: a posit logarithm-approximate multiplier
spellingShingle PLAM: a posit logarithm-approximate multiplier
Murillo Montero, Raúl
004.3
621.38
Posit arithmetic
Arithmetic and logic structures
Low-power design
Machine learning
Computer vision
Informática (Informática)
Hardware
1203 Ciencia de Los Ordenadores
3307.03 Diseño de Circuitos
title_short PLAM: a posit logarithm-approximate multiplier
title_full PLAM: a posit logarithm-approximate multiplier
title_fullStr PLAM: a posit logarithm-approximate multiplier
title_full_unstemmed PLAM: a posit logarithm-approximate multiplier
title_sort PLAM: a posit logarithm-approximate multiplier
dc.creator.none.fl_str_mv Murillo Montero, Raúl
Del Barrio García, Alberto Antonio
Botella Juan, Guillermo
Kim, Min Soo
Kim, HyunJin
Bagherzadeh, Nader
author Murillo Montero, Raúl
author_facet Murillo Montero, Raúl
Del Barrio García, Alberto Antonio
Botella Juan, Guillermo
Kim, Min Soo
Kim, HyunJin
Bagherzadeh, Nader
author_role author
author2 Del Barrio García, Alberto Antonio
Botella Juan, Guillermo
Kim, Min Soo
Kim, HyunJin
Bagherzadeh, Nader
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Universidad Complutense de Madrid
dc.subject.none.fl_str_mv 004.3
621.38
Posit arithmetic
Arithmetic and logic structures
Low-power design
Machine learning
Computer vision
Informática (Informática)
Hardware
1203 Ciencia de Los Ordenadores
3307.03 Diseño de Circuitos
topic 004.3
621.38
Posit arithmetic
Arithmetic and logic structures
Low-power design
Machine learning
Computer vision
Informática (Informática)
Hardware
1203 Ciencia de Los Ordenadores
3307.03 Diseño de Circuitos
description The Posit™ Number System was introduced in 2017 as a replacement for floating-point numbers. Since then, the community has explored its application in several areas, such as deep learning, and produced some unit designs which are still far from being competitive with their floating-point counterparts. This article proposes a Posit Logarithm-Approximate Multiplication (PLAM) scheme to significantly reduce the complexity of posit multipliers, one of the most power-hungry arithmetic units. The impact of this approach is evaluated in deep neural network inference, where there are no significant accuracy drops. Compared with state-of-the-art posit multipliers, experiments show that the proposed technique reduces the area, power, and delay of 32-bit hardware multipliers up to 72.86%, 81.79%, and 17.01%, respectively.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-09-06
2021
2021-09-06
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14352/132729
url https://hdl.handle.net/20.500.14352/132729
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 RTI2018-093684-B-I00 HETEROGENEIDAD Y ESPECIALIZACION EN LA ERA POST-MOORE
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
dc.source.none.fl_str_mv reponame:Docta Complutense
instname:Universidad Complutense de Madrid (UCM)
instname_str Universidad Complutense de Madrid (UCM)
reponame_str Docta Complutense
collection Docta Complutense
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869409937337090048
score 15,812429

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