An Alternative TAGE-like Conditional Branch Predictor

International audienceTAGE is one of the most accurate conditional branch predictors known today. However, TAGE does not exploit its input information perfectly, as it is possible to obtain significant prediction accuracy improvements by complementing TAGE with a statistical corrector using the same input information. This paper proposes an alternative TAGE-like predictor making statistical correction practically superfluous