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2002
IEEE

Predicting Conditional Branches With Fusion-Based Hybrid Predictors

10 years 3 months ago
Predicting Conditional Branches With Fusion-Based Hybrid Predictors
Researchers have studied hybrid branch predictors that leverage the strengths of multiple stand-alone predictors. The common theme among the proposed techniques is a selection mechanism that chooses a prediction from among several component predictors. We make the observation that singling out one particular component predictor ignores the information of the non-selected components. We propose Branch Prediction Fusion, originally inspired by work in the machine learning field, which combines or fuses the information from all of the components to arrive at a final prediction. Our 32KB predictor achieves the same overall prediction accuracy as the 188KB versions of the previous best performing predictors (the Multi-Hybrid and the global-local perceptron).
Gabriel H. Loh, Dana S. Henry
Added 15 Jul 2010
Updated 15 Jul 2010
Type Conference
Year 2002
Where IEEEPACT
Authors Gabriel H. Loh, Dana S. Henry
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