Predicting Conditional Branches With Fusion-Based Hybrid Predictors

  • Authors:
  • Gabriel H. Loh;Dana S. Henry

  • Affiliations:
  • -;-

  • Venue:
  • Proceedings of the 2002 International Conference on Parallel Architectures and Compilation Techniques
  • Year:
  • 2002

Quantified Score

Hi-index 0.00

Visualization

Abstract

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 bywork 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-allprediction accuracy as the 188KB versions of the previous best performing predictors (the Multi-Hybrid and the global-local perceptron).