Proceedings of the 22nd International Conference on Parallel Architectures and Compilation Techniques (2002)
Sept. 22, 2002 to Sept. 25, 2002
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).
Gabriel H. Loh, Dana S. Henry, "Predicting Conditional Branches With Fusion-Based Hybrid Predictors", Proceedings of the 22nd International Conference on Parallel Architectures and Compilation Techniques, vol. 00, no. , pp. 165, 2002, doi:10.1109/PACT.2002.1106015