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

With the pipeline deepen and issue width widen, the accuracy of branch predictor becomes more and more important 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 perceptron 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 simulation results show that with the storage budget changing from 32 kbits to 512 Kbits 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 saving computation is the saved power consumption on the predictor which is also an important factor in nowadays microprocessor.