A WiSARD-based conditional branch predictor - Villon, L. A. Q.; Susskind, Z.; Bacellar, A. T. L.; Miranda, I. D. S.; Araújo, L. S. de.; Lima, P. M. V.; Breternitz Jr, M.; John, L. K.; França, F. M. G.; Dutra, D. L. C. (ESANN 2022 proceedings, 2022)
Conditional branch prediction is a technique used to speculatively execute instructions before knowing the direction of conditional branch statements. Perceptron-based predictors have been extensively studied, however, they need large input sizes for the data to be linearly separable. To learn nonlinear functions from the inputs, we propose a conditional branch predictor based on the WiSARD model and compare it with two state-of-the-art predictors, the TAGE-SC-L and the Multiperspective Perceptron. We show that the WiSARD-based predictor with a smaller input size outperforms the perceptron-based predictor by about 0.09% and achieves similar accuracy to that of TAGE-SC-L.