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.

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• Engenharia Eletrotécnica, Eletrónica e Informática - Engenharia e Tecnologia

Esta publicação é um output do(s) seguinte(s) projeto(s):

• Alternative Shaper
• O Impacto das Tecnologias Blockchains e de registo descentralizado