A Computation Saving Partial-Sum-Global-Update Scheme For Perceptron Branch Predictor

With the pipeline deepen and issue width widen, the ac-curacy of branch predictor becomes more and more impor-tant to the performance of a microprocessor. State-of-the-art researches have shown that perceptron branch predictor can obtain a higher accuracy than the existing widely used table based branch predictor. One shortcoming of percep-tron branch predictor is the high prediction latency which most comes from the computation needed by the predicting process. In this paper, we propose a Partial-Sum-Global-Update scheme to decrease the number of computation of perceptron predictor with marginal accuracy losing. This scheme is orthogonal to the other schemes such as ahead pipelining. Using O-GEHL predictor as example, the sim-ulation results show that with the storage budget changing from 32kbits to 512Kbits our scheme can save up to 18.2% of computation for a prediction in average as while as only losing up to 1.75 % accuracy. Another benefit from the sav-ing computation is the saved power consumption on the pre-dictor which is also an important factor in nowadays micro-processor