Implementation of a block based neural branch predictor

Abstract

This paper contributes to a dynamic branch predictor algorithm based on a perceptron in two directions: Firstly, a new block form of computation is introduced that reduces theoretically by half the combinational critical path for computing a prediction. Secondly, implementation in FPGA hardware is fully developed for quantitative comparison purposes. FPGA circuits for a one-cycle block predictor produces 1.7 faster clock rates than a direct implementation of the original perceptron predictor. This faster clock allows to realize predictions with longer history lengths for the same hardware budget.