Idealized Piecewise Linear Branch Prediction

Traditional branch predictors exploit correlations between pattern history and branch outcome to predict branches, but there is a stronger and more natural correlation between path history and branch outcome. I exploit this correlation with piecewise linear branch prediction, an idealized branch predictor that develops a set of linear functions, one for each program path to the branch to be predicted, that separate predicted taken from predicted not taken branches. Taken together, all of these linear functions form a piecewise linear decision surface. Disregarding implementation concerns modulo a 64.25 kilobit hardware budget, I present this idealized branch predictor for the first Championship Branch Predictor competition. I describe the idea of the algorithm and as well as tricks used to squeeze it into 64.25 kilobits while maintaining good accuracy.