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Branch predictor on-line evolutionary system

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Karel SlanyAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Pages 1643 - 1648

https://doi.org/10.1145/1389095.1389404

Published: 12 July 2008 Publication History

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Abstract

In this work a branch prediction system which utilizes evolutionary techniques is introduced. It allows the predictor to adapt to the executed code and thus to improve its performance on the fly. Experiments with the predictor system were performed and the results display how various parameters can impact its performance on various executed code. It is evident that a one-level predictor can be evolved whose performance is better than comparable predictors of the same class. The dynamic prediction system predicts with a relative high accuracy and outperforms any static predictor of the same class.

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Lanchares JGarnica OFernández‐de‐Vega FIgnacio Hidalgo J(2013)A review of bioinspired computer‐aided design tools for hardware designConcurrency and Computation: Practice and Experience10.1002/cpe.295725:8(1015-1036)Online publication date: 2-Jan-2013
https://doi.org/10.1002/cpe.2957
Slany K(2009)Towards the Automatic Evolutionary Prediction of the FOREX Market BehaviourProceedings of the 2009 International Conference on Adaptive and Intelligent Systems10.1109/ICAIS.2009.31(141-145)Online publication date: 24-Sep-2009
https://dl.acm.org/doi/10.1109/ICAIS.2009.31

Index Terms

Branch predictor on-line evolutionary system
1. Computing methodologies
  1. Machine learning

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Published In

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

July 2008

1814 pages

ISBN:9781605581309

DOI:10.1145/1389095

Conference Chair:
Conor Ryan
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 12 July 2008

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GECCO08: Genetic and Evolutionary Computation Conference

July 12 - 16, 2008

GA, Atlanta, USA

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Lanchares JGarnica OFernández‐de‐Vega FIgnacio Hidalgo J(2013)A review of bioinspired computer‐aided design tools for hardware designConcurrency and Computation: Practice and Experience10.1002/cpe.295725:8(1015-1036)Online publication date: 2-Jan-2013
https://doi.org/10.1002/cpe.2957
Slany K(2009)Towards the Automatic Evolutionary Prediction of the FOREX Market BehaviourProceedings of the 2009 International Conference on Adaptive and Intelligent Systems10.1109/ICAIS.2009.31(141-145)Online publication date: 24-Sep-2009
https://dl.acm.org/doi/10.1109/ICAIS.2009.31

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Recommendations

NTB branch predictor: dynamic branch predictor for high-performance embedded processors

A novel meta predictor design for hybrid branch prediction

Design tradeoffs for the Alpha EV8 conditional branch predictor

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