# A Survey of Deep Learning Techniques for Dynamic Branch Prediction

arXiv: https://arxiv.org/abs/2112.14911 · HTML: https://mlsanity.com/paper/2112.14911/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 21 references that are in the map.
- First posted on arXiv 30 Dec 2021.

## Builds on (most cited first, 4 of 4)

- [The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design](https://mlsanity.com/paper/1911.05289/) (2019)
- [Improving Branch Prediction By Modeling Global History with Convolutional Neural Networks](https://mlsanity.com/paper/1906.09889/) (2019)
- [A Survey of Techniques for Dynamic Branch Prediction](https://mlsanity.com/paper/1804.00261/) (2018)
- [Branch Prediction as a Reinforcement Learning Problem: Why, How and Case Studies](https://mlsanity.com/paper/2106.13429/) (2021)

## Built on by (most cited first, 1 of 1)

- [Using Machine Learning to Take Stay-or-Go Decisions in Data-driven Drone Missions](https://mlsanity.com/paper/2512.04773/) (2025)

## Abstract (arXiv)

Branch prediction is an architectural feature that speeds up the execution of branch instruction on pipeline processors and reduces the cost of branching. Recent advancements of Deep Learning (DL) in the post Moore's Law era is accelerating areas of automated chip design, low-power computer architectures, and much more. Traditional computer architecture design and algorithms could benefit from dynamic predictors based on deep learning algorithms which learns from experience by optimizing its parameters on large number of data. In this survey paper, we focus on traditional branch prediction algorithms, analyzes its limitations, and presents a literature survey of how deep learning techniques can be applied to create dynamic branch predictors capable of predicting conditional branch instructions. Prior surveys in this field focus on dynamic branch prediction techniques based on neural network perceptrons. We plan to improve the survey based on latest research in DL and advanced Machine Learning (ML) based branch predictors.