# A Survey of Techniques for Dynamic Branch Prediction

arXiv: https://arxiv.org/abs/1804.00261 · HTML: https://mlsanity.com/paper/1804.00261/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2018.

## Built on by (most cited first, 3 of 3)

- [SymForce: Symbolic Computation and Code Generation for Robotics](https://mlsanity.com/paper/2204.07889/) (2022)
- [A Survey of Deep Learning Techniques for Dynamic Branch Prediction](https://mlsanity.com/paper/2112.14911/) (2021)
- [Using Machine Learning to Take Stay-or-Go Decisions in Data-driven Drone Missions](https://mlsanity.com/paper/2512.04773/) (2025)

## Abstract (arXiv)

Branch predictor (BP) is an essential component in modern processors since high BP accuracy can improve performance and reduce energy by decreasing the number of instructions executed on wrong-path. However, reducing latency and storage overhead of BP while maintaining high accuracy presents significant challenges. In this paper, we present a survey of dynamic branch prediction techniques. We classify the works based on key features to underscore their differences and similarities. We believe this paper will spark further research in this area and will be useful for computer architects, processor designers and researchers.