Exploring Deep Learning-based Branch Prediction for Computer Devices

Abstract

Branch predictor is a critical component in CPUs because its prediction accuracy highly influences the performance of computer devices. This technology attempts to predict whether a branch instruction is ‘taken’ or ‘not taken’ and executes the following instructions in an execution order based on the prediction result. If the prediction is incorrect, those speculatively executed instructions must be rolled back, causing overheads on both performance and energy efficiency. Conventional branch predictors typically adopt rule-based methods exploiting branch history (i.e., whether recently encountered branches in the course of execution or on the same address of the current instruction were taken or not), whereas deep learning-based prediction methods have been recently proposed. In this paper, we show the neural network model learned with less dataset generalizes well for all applications, not just for specific applications in the training set. Also, unlike the previous deep learning-based branch prediction studies, which were difficult to reproduce, this paper includes clear experiment contents.