Abstract: With the increasing demand for processor performance,superscalar and deeply pipelined techniques have been widely adopted in modern microprocessors to enhance instruction-level parallelism.However,conditional branch instructions in programs pose a challenge to continuous pipeline execution,limiting the potential for parallel instruction processing.To address this control hazard,branch prediction techniques have been developed,with the core objective of speculatively determining the direction and target address of branch instructions,thereby minimizing pipeline stalls caused by branch instructions.This paper presents a comparative analysis of two mainstream branch predictors—TAGE-based and Neural-based approaches—under a unified performance evaluation framework.Experimental results demonstrate that different branch predictors exhibit distinct preferences for specific traces,suggesting that hybrid prediction mechanisms could further unlock prediction potential.Additionally,the influence of execution context on branch prediction performance cannot be overlooked,particularly in multi-process environments.Furthermore,this paper reveals that current CNN-based predictors exhibit unstable performance when handling complex branch patterns,with their overall accuracy yet to surpass the baseline TAGE-SC-L predictor,indicating a need for further optimization.

Key words: branch prediction, tagged geometric history length(TAGE), neural network, perceptron, convolutional neural network(CNN) model