# Branch Prediction Is Not a Solved Problem: Measurements, Opportunities, and Future Directions

## TL;DR

This paper analyzes the limitations of current branch predictors, identifies the causes of mispredictions, and proposes future research directions including machine learning and phase learning to improve branch prediction accuracy.

## Contribution

It provides a detailed measurement and characterization of branch mispredictions, revealing their root causes and suggesting new approaches beyond resource scaling.

## Key findings

- Mispredictions mainly stem from hard-to-predict and rare branches.
- Increasing predictor resources alone does not fix most mispredictions.
- Deploying machine learning and phase learning can address fundamental prediction challenges.

## Abstract

Modern branch predictors predict the vast majority of conditional branch instructions with near-perfect accuracy, allowing superscalar, out-of-order processors to maximize speculative efficiency and thus performance. However, this impressive overall effectiveness belies a substantial missed opportunity in single-threaded instructions per cycle (IPC). For example, we show that correcting the mispredictions made by the state-of-the-art TAGE-SC-L branch predictor on SPECint 2017 would improve IPC by margins similar to an advance in process technology node. In this work, we measure and characterize these mispredictions. We find that they categorically arise from either (1) a small number of systematically hard-to-predict (H2P) branches; or (2) rare branches with low dynamic execution counts. Using data from SPECint 2017 and additional large code footprint applications, we quantify the occurrence and IPC impact of these two categories. We then demonstrate that increasing the resources afforded to existing branch predictors does not alone address the root causes of most mispredictions. This leads us to reexamine basic assumptions in branch prediction and to propose new research directions that, for example, deploy machine learning to improve pattern matching for H2Ps, and use on-chip phase learning to track long-term statistics for rare branches.