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Proceedings.International Conference on Parallel Architectures and Compilation Techniques (2002)
Charlottesville, Virginia
Sept. 22, 2002 to Sept. 25, 2002
ISSN: 1089-795X
ISBN: 0-7695-1620-3
pp: 165
Gabriel H. Loh , Yale University
Dana S. Henry , Yale University
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 over-all prediction accuracy as the 188KB versions of the previous best performing predictors (the Multi-Hybrid and the global-local perceptron).

G. H. Loh and D. S. Henry, "Predicting Conditional Branches With Fusion-Based Hybrid Predictors," Proceedings.International Conference on Parallel Architectures and Compilation Techniques(PACT), Charlottesville, Virginia, 2002, pp. 165.
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