RUNLTS: Branch Prediction with Register-Value Correlations and Hierarchical Table Orchestration

The TAgged GEometric history length predictor (TAGE) and its derivatives are widely regarded as among the most accurate branch predictors. In particular, the TAGE-SC predictor, which combines TAGE with a statistical corrector (SC), achieves state-of-the-art prediction accuracy. In this paper, we propose RUNLTS, a novel branch predictor that augments TAGE-SC with structural refinements and a novel value-correlation-based component, RBias. On the structural side, RUNLTS redesigns the allocation policy, reshapes the history length set, and reorganizes the statistical corrector. RBias is a novel prediction mechanism that directly learns correlations between branch outcomes and register values without explicitly tracking data-dependence chains. It exploits a broad range of value-branch correlations that conventional predictors fail to capture. Our simulation results showed that the RUNLTS predictor significantly outperforms the baseline TAGE-SC in prediction accuracy.