Machine learning for branch analysis

Abstract

A processing unit executes a sequence of instructions comprising a branch instruction and selects a target branch predictor for the branch instruction from a plurality of branch predictors. The selection is based on a language model trained on a set of training instruction sequences that comprise branch instructions. The target branch predictor then determines the outcome of the branch instruction. The language model is trained to identify the branch instructions as having easy-to-predict outcomes or hard-to-predict outcomes based on sequence of instructions. Information generated by the language model is provided to a branch prediction unit, which uses the information to determine whether branch instructions are easy-to-predict or hard-to-predict.

Dependencies are inferred from the claim text.