Branch classification: a new mechanism for improving branch predictor performance
MICRO 27 Proceedings of the 27th annual international symposium on Microarchitecture
Dynamic Branch Prediction with Perceptrons
HPCA '01 Proceedings of the 7th International Symposium on High-Performance Computer Architecture
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Branch taken rate and transition rate have been proposed as metrics to characterize the branch predictability. However, these two metrics may misclassify branches with regular history patterns as hard-to-predict branches, causing an inaccurate and ambiguous view of branch predictability. This study uses autocorrelation to analyze the branch history patterns and presents a new metric Degree of Pattern Irregularity (DPI) for branch classification. The proposed metric is evaluated with different branch predictors, and the results show that DPI significantly improves the quality and the accuracy of branch classification over traditional taken rate and transition rate.