Abstract
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.
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Loh, G.H., Henry, D.S. (2002). Applying Machine Learning for Ensemble Branch Predictors. In: Hendtlass, T., Ali, M. (eds) Developments in Applied Artificial Intelligence. IEA/AIE 2002. Lecture Notes in Computer Science(), vol 2358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48035-8_26
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DOI: https://doi.org/10.1007/3-540-48035-8_26
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