An optimized scaled neural branch predictor

Abstract

Conditional branch prediction remains one of the most important enabling technologies for high-performance microprocessors. A small improvement in accuracy can result in a large improvement in performance as well as a significant reduction in energy wasted on wrong-path instructions. Neural-based branch predictors have been among the most accurate in the literature. The recently proposed scaled neural analog predictor, or SNAP, builds on piecewise-linear branch prediction and relies on a mixed analog/digital implementation to mitigate latency as well as power requirements over previous neural predictors. We present an optimized version of the SNAP predictor, hybridized with two simple two-level adaptive predictors. The resulting optimized predictor, OH-SNAP, delivers very high accuracy compared with other state-of-the-art predictors.