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dc.date.accessioned 2008-05-22T18:49:06Z
dc.date.available 2008-05-22T03:00:00Z
dc.date.issued 2007-04
dc.identifier.uri http://sedici.unlp.edu.ar/handle/10915/9550
dc.description.abstract The O-GEHL branch predictor has outperformed other prediction schemes using the same set of benchmarks in an international branch prediction contest, CBP-1. In this paper, we present the analysis results on each of the OGEHL branch predictor tables and also on the optimal number of predictor tables. Two methods are subsequently proposed to help increase the O-GEHL prediction accuracy. The first one aims to increase the space utilization of the first predictor table by dynamically adjusting the lengths of branch history regarding to the type of a benchmark currently in execution. The second one adds an extra table into the O-GEHL predictor using the space saved from the sharing of hysteresis bits. Experimental results have confirmed that both schemes improve the accuracy of two different predictor configurations, leading to two promising research directions for future explorations. en
dc.format.extent 171-176 es
dc.language en es
dc.subject Neural nets es
dc.subject branch predictor en
dc.subject perceptron en
dc.subject predictor analysis en
dc.title Improving the O-GEHL branch prediction accuracy using analytical results en
dc.type Articulo es
sedici.identifier.uri http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Apr07-7.pdf es
sedici.identifier.issn 1666-6038 es
sedici.creator.person Tiamkaew, Ekkasit es
sedici.creator.person Kongmunvattana, Angkul es
sedici.subject.materias Ciencias Informáticas es
sedici.description.fulltext true es
mods.originInfo.place Facultad de Informática es
sedici.subtype Articulo es
sedici.rights.license Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
sedici.rights.uri http://creativecommons.org/licenses/by-nc/3.0/
sedici.description.peerReview peer-review es
sedici2003.identifier ARG-UNLP-ART-0000000593 es
sedici.relation.journalTitle Journal of Computer Science & Technology es
sedici.relation.journalVolumeAndIssue vol. 7, no. 2 es


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Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0) Excepto donde se diga explícitamente, este item se publica bajo la siguiente licencia Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)