Global/Local Hashed Perceptron Branch Prediction

This paper introduces combining local history hashing and global history hashing in perceptron branch prediction. The proposed perceptron predictor utilizes self-history as well as global history in indexing different weights of a perceptron. The simulation results show that our proposed perceptron predictor is more accurate than the one using either global history hashing or local history hashing alone. Our proposed perceptron predictor improves the misprediction rate by up to 26.9% over path-based neural predictor, and 17.2% over hashed perceptron predictor. A reducing aliasing approach is employed to improve the accuracy of our proposed perceptron predictor by diminishing the impact of destructive interference, supported by the simulation results.