Perceptron-Based Branch Confidence Estimation

Abstract

Pipeline gating has been proposed for reducing wasted speculative execution due to branch mispredictions. As processors become deeper or wider, pipeline gating becomes more important because the amount of wasted speculative execution increases. The quality of pipeline gating relies heavily on the branch confidence estimator used. Not much work has been done on branch confidence estimators since the initial work [6]. We show the accuracy and coverage characteristics of the initial proposals do not sufficiently reduce mis-speculative execution on future deep pipeline processors. In this paper, we present a new, perceptron-based, branch confidence estimator, which is twice as accurate as the current best-known method and achieves reasonable mispredicted branch coverage. Further, the output of our predictor is multi-valued, which enables us to classify branches further as "strongly low confident" and "weakly low confident". We reverse the predictions of "strongly low confident" branches and apply pipeline gating to the "weakly low confident" branches. This combination of pipeline gating and branch reversal provides a spectrum of interesting design options ranging from significantly reducing total execution for only a small performance loss, to lower but still significant reductions in total execution, without any performance loss.