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Cached Two-Level Adaptive Branch Predictors with Multiple Stages

  • Colin Egan
  • Gordon Steven
  • Lucian Vintan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2299)

Abstract

In this paper, we quantify the performance of a novel family of multi-stage Two-Level Adaptive Branch Predictors. In each two- level predictor, the PHT of a conventional Two-level Adaptive Branch Predictor is replaced by a Prediction Cache. Unlike a PHT, a Prediction Cache saves only relevant branch prediction information. Furthermore, predictions are never based on uninitialised entries and interference between branches is eliminated. In the case of a Prediction Cache miss in the first stage, our two-stage predictors use a default two-bit prediction counter stored in a second stage. We demonstrate that a two- stage Cached Predictor is more accurate than a conventional two-level predictor and quantify the crucial contribution made by the second prediction stage in achieving this high accuracy. We then extend our Cached Predictor by adding a third stage and demonstrate that a Three- Stage Cached Predictor further improves the accuracy of cached predictors.

Keywords

Cache Size Branch Prediction Prediction Stage History Register Branch Predictor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Colin Egan
    • 1
  • Gordon Steven
    • 1
  • Lucian Vintan
    • 2
  1. 1.University of HertfordshireHatfieldUK
  2. 2.University “Lucian Braga” of SibiuRomania

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