Combining Local and Global History Hashing in Perceptron Branch Prediction

As the instruction issue rate and depth of pipelining increase, branch prediction is considered as a performance hurdle for modern processors. Extremely high branch prediction accuracy is essential to deliver their potential performance. Many perceptron branch predictors have been investigated to improve the dynamic branch prediction in recent years. This paper introduces combining local history hashing and global history hashing in perceptron branch prediction. This 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 is able to achieve 4.13% misprediction rate and even 0.45% misprediction rate in some cases. And it has an improvement of 9.21% over using global history hashing alone, the mapping scheme proposed by Tarjan and Skadron.

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