Since 2006, TAGE has been often considered as state-of-the-art in terms of branch prediction accuracy [1, 5] In this paper, we reenforce the case for the TAGE predictor in two directions First we show that the TAGE predictor requires less accesses to the predictor tables than other branch predictors thus potentially
other TAGE predictor congurations Section 5 briey re-views the related works that had major inuences in the L-TAGE predictor proposition and discusses a few tradeoffs that might inuence the choice of a TAGE conguration for an effective implementation 1 The TAGE conditional branch predictor The TAGE predictor is derived from Michaud’s PPM-
Geometric length (TAGE) branch predictors are some of the best predictors proposed in academia [1] Although several papers describe the performance of TAGE through trace- and execution-driven simulation [2] [3], there is limited prior work on detailed hardware implementations Practical designs of TAGE assume speculative updates of
to use the perceptron prediction or the TAGE prediction This algorithm is the same as that used in TAGE-SC-L; the novel part is the augmentation of the perceptron predictor with the features described above 4 2 Updating the Predictor To update the predictor, first the loop and TAGE predictors are updated as described in the literature on
This project is funded in part by a grant from the National Geographic Society Education Foundation Physical Geography of Southeast Asia: Module Materials
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