Adaptive Information Processing: An Effective Way to Improve Perceptron Branch Predictors

Perceptron branch predictors achieve high prediction accuracy by capturing correlation from very long histories. The required hardware, however, limits the history length to be explored practically. In this paper, an important observation is made that the perceptron weights can be used to estimate the strength of branch correlation. Based such an estimate, adaptive schemes are proposed to preprocess history information so that the input vector to a perceptron predictor contains only those history bits with the strongest correlation. In this way, a much larger history-information set can be explored effectively without increasing the size of perceptron predictors. For the distributed Championship Branch Prediction (CBP-1) traces, our proposed scheme achieves a 47% improvement over a g-share predictor of the same size. For SPEC2000 benchmarks, our proposed scheme outperforms the g-share predictor by 35% on average.