Branch prediction on demand: an energy-efficient solution

Authors:
Daniel Chaver;Luis Piñuel;Manuel Prieto;Francisco Tirado;Michael C. Huang
Affiliations:
Universidad Complutense, Madrid, Spain;Universidad Complutense, Madrid, Spain;Universidad Complutense, Madrid, Spain;Universidad Complutense, Madrid, Spain;University of Rochester, Rochester, New York
Venue:
Proceedings of the 2003 international symposium on Low power electronics and design
Year:
2003

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Abstract

High-end processors typically incorporate complex branch predictors consisting of many large structures that together consume a notable fraction of total chip power (more than 10% in some cases). Depending on the applications, some of these resources may remain underused for long periods of time. We propose a methodology to reduce the energy consumption of the branch predictor by characterizing prediction demand using profiling and dynamically adjusting predictor resources accordingly. Specifically, we disable components of the hybrid direction predictor and resize the branch target buffer. Detailed simulations show that this approach reduces the energy consumption in the branch predictor by an average of 72% and up to 89% with virtually no impact on prediction accuracy and performance.