Storage free confidence estimation for the TAGE branch predictor

Abstract

For the past 15 years, it has been shown that confidence estimation of branch prediction can be used for various usages such as fetch gating or throttling for power saving or for controlling resource allocation policies in a SMT processor. In many proposals, using extra hardware and particularly storage tables for branch confidence estimators has been considered as a worthwhile silicon investment. The TAGE predictor presented in 2006 is so far considered as the state-of-the-art conditional branch predictor. In this paper, we show that very accurate confidence estimations can be done for the branch predictions performed by the TAGE predictor by simply observing the outputs of the predictor tables. Many confidence estimators proposed in the literature only discriminate between high confidence predictions and low confidence predictions. It has been recently pointed out that a more selective confidence discrimination could useful. We show that the observation of the outputs of the predictor tables is sufficient to grade the confidence in the branch predictions with a very good granularity. Moreover a slight modification of the predictor automaton allows to discriminate the prediction in three classes, low-confidence (with a misprediction rate in the 30 % range), medium confidence (with a misprediction rate in 8–12% range) and high confidence (with a misprediction rate lower than 1 %).