Low-Power Perceptron Branch Predictor

Perceptron based predictors are highly accurate. This high accuracy is the result of exploiting long history lengths [1] and is achieved at the expense of high complexity. The dot product of two vectors is used as prediction. The first vector is the branch outcome history where the second vector is composed of per branch weights, which represent the correlation between branch outcome and previously encountered branch instruction outcomes. The sign of the dot product determines the prediction. A non-negative value represents taken and a negative value represents not taken.