A Multi-model Power Estimation Engine For Accuracy Optimization

dc.creatorKlein F.
dc.creatorAraujo G.
dc.creatorAzevedo R.
dc.creatorLeao R.
dc.creatorDos Santos L.C.V.
dc.date2007
dc.date2015-06-30T18:51:40Z
dc.date2015-11-26T14:38:52Z
dc.date2015-06-30T18:51:40Z
dc.date2015-11-26T14:38:52Z
dc.date.accessioned2018-03-28T21:44:12Z
dc.date.available2018-03-28T21:44:12Z
dc.identifier1595937099; 9781595937094
dc.identifierProceedings Of The International Symposium On Low Power Electronics And Design. , v. , n. , p. 280 - 285, 2007.
dc.identifier15334678
dc.identifier10.1145/1283780.1283840
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-36949030673&partnerID=40&md5=f35baac5c55147f810b2de8609aa927a
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/105115
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/105115
dc.identifier2-s2.0-36949030673
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1249767
dc.descriptionRTL power macromodeling is a mature research topic with a variety of equation and table-based approaches. Despite its maturity, macromodeling is not yet widely accepted as an industrial de facto standard for power estimation at the RT level. Each approach has many variants depending upon the parameters chosen to capture power variation. Every macromodeling technique has some intrinsic limitation affecting either its performance or its accuracy. Therefore, alternative macromodeling methods can be envisaged as part of a power modeling toolkit from which the most suitable method for a given component should be automatically selected. Thispaper describes a new multi-model power estimation engine that selects the macromodeling technique leading to the least estimation error for a given system component depending on the properties of its input-vector stream. A proper selection function is built after component characterization and used during estimation. Experimental results show that our multi-model engine improves the robustness of power analysis with negligible usage overhead. Accuracy becomes 3 times better on average, as compared to conventional single-model estimators, while the overall maximum estimation error is divided by 8. Copyright 2007 ACM.
dc.description
dc.description
dc.description280
dc.description285
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dc.languageen
dc.publisher
dc.relationProceedings of the International Symposium on Low Power Electronics and Design
dc.rightsfechado
dc.sourceScopus
dc.titleA Multi-model Power Estimation Engine For Accuracy Optimization
dc.typeActas de congresos


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