Enhancing branch predictors using genetic algorithm

Title: Enhancing branch predictors using genetic algorithm
Creator: Haque, Md Sarwar; Hassan, Md Rafiul; Sulaiman, Muhammad; Onoruoiza, Salami; Kamruzzaman, Joarder; Arifuzzaman, Md
Date: 2019
Type: Text; Conference proceedings; Conference paper
Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/171989
Identifier: vital:14420
Identifier: https://doi.org/10.1109/ICMSAO.2019.8880435
Identifier: ISBN:9781538676844 (ISBN)
Abstract: Dynamic branch prediction is a hardware technique used to speculate the direction of control branches. Inaccurate prediction will make all speculative works useless while accurate prediction will significantly improve microprocessors performance. In this work, we have shown that Genetic Algorithm (GA) can be used to select (near) optimal parameters for branch predictors in most cases. The GA-enhanced predictors take time to find suitable parameters, but once the values of these parameters are determined, the GA-enhanced predictors take the same time to execute as the basic predictors with increased accuracy. © 2019 IEEE.; E1
Publisher: Institute of Electrical and Electronics Engineers Inc.
Relation: 8th International Conference on Modeling Simulation and Applied Optimization, ICMSAO 2019
Rights: This metadata is freely available under a CCO license
Subject: Branch prediction; Genetic algorithm; Neural network
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