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.

翻译：部门预测是一个建筑特征,它加快了管道处理器分支指令的执行,并降低了分流成本。摩尔法时代后深入学习(DL)最近的进展正在加速自动芯片设计、低功率计算机结构以及更多领域。传统的计算机结构设计和算法可以受益于基于深层次学习算法的动态预测器,这些算法通过优化大量数据的参数而从经验中学习。在本调查文件中,我们侧重于传统的分支预测算法,分析其局限性,并介绍如何运用深层次学习技术来创建能预测有条件分支指令的动态分支预测器。先前的实地调查侧重于基于神经网络的动态分支预测技术。我们计划根据对DL和高级机器学习(ML)分支预测器的最新研究改进调查。