A Power-Aware Alternative for the Perceptron Branch Predictor

The perceptron predictor is a highly accurate branch predictor. Unfortunately this high accuracy comes with high complexity. The high complexity is the result of the large number of computations required to speculate each branch outcome. In this work we aim at reducing the computational complexity for the perceptron predictor. We show that by eliminating unnecessary data from computations, we can reduce both predictor’s power dissipation and delay. We show that by applying our technique, predictor’s dynamic and static power dissipation can be reduced by up to 52% and 44% respectively. Meantime we improve performance by up to 16% as we make faster prediction possible.