Split last-address predictor

Abstract — Recent works have proposed the use of prediction techniques to execute speculatively true data-dependent operations. However, the predictability of the operations do not spread uniformly among them. Then, we propose the use of run-time classification of instructions to increase the efficiency of the predictors. At run time, the proposed mechanism classifies instructions according to their predictability, decoupling this classification from prediction table. Then, the classification is used to avoid the unpredictable instructions from being candidates to allocate an entry in the prediction table. The previous idea of run-time classification is applied to the last-address predictor (Spit Last-Address Predictor). The goal of this predictor is to reduce the latency of load instructions. Memory access is performed after the effective address is predicted concurrently with instruction fetch, after that, next true data-dependent instructions can be executed speculatively. We show that our proposal applied to the last-address predictor captures the same predictability than the last-address predictor proposed in literature, increases its accuracy, and reduces its area-cost a 19%