Universal forecasting algorithms
Information and Computation
The weighted majority algorithm
Information and Computation
ISCA '96 Proceedings of the 23rd annual international symposium on Computer architecture
Value locality and load value prediction
Proceedings of the seventh international conference on Architectural support for programming languages and operating systems
Evidence-based static branch prediction using machine learning
ACM Transactions on Programming Languages and Systems (TOPLAS)
Assigning confidence to conditional branch predictions
Proceedings of the 29th annual ACM/IEEE international symposium on Microarchitecture
Trading conflict and capacity aliasing in conditional branch predictors
Proceedings of the 24th annual international symposium on Computer architecture
A language for describing predictors and its application to automatic synthesis
Proceedings of the 24th annual international symposium on Computer architecture
MICRO 30 Proceedings of the 30th annual ACM/IEEE international symposium on Microarchitecture
Prediction caches for superscalar processors
MICRO 30 Proceedings of the 30th annual ACM/IEEE international symposium on Microarchitecture
Memory dependence prediction using store sets
Proceedings of the 25th annual international symposium on Computer architecture
The YAGS branch prediction scheme
MICRO 31 Proceedings of the 31st annual ACM/IEEE international symposium on Microarchitecture
The Alpha 21264 Microprocessor
IEEE Micro
Dynamic Branch Prediction with Perceptrons
HPCA '01 Proceedings of the 7th International Symposium on High-Performance Computer Architecture
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The problem of predicting the outcome of a conditional branch instruction is a prerequisite for high performance in modern processors. It has been shown that combining different branch predictors can yield more accurate prediction schemes, but the existing research only examines selection-based approaches where one predictor is chosen without considering the actual predictions of the available predictors. The machine learning literature contains many papers addressing the problem of predicting a binary sequence in the presence of an ensemble of predictors or experts. We show that the Weighted Majority algorithm applied to an ensemble of branch predictors yields a prediction scheme that results in a 5-11% reduction in mispredictions. We also demonstrate that a variant of the Weighted Majority algorithm that is simplified for efficient hardware implementation still achieves misprediction rates that are within 1.2% of the ideal case.