Attention-Based Branch Prediction

This project uses a Transformer-based branch predictor as an oracle predictor to systematically study branch prediction behavior. By training a high-capacity Transformer model to achieve near-optimal prediction accuracy, the model serves as an analysis tool to evaluate predictor performance, stability, and feature dependence. Based on feature ablation results, the project explores how to optimize conventional branch predictors and provides guidance for next-generation predictor design.