计算机应用研究2024，Vol.41Issue(4)：1270-1274,5.DOI:10.19734/j.issn.1001-3695.2023.08.0385
基于视觉强化学习的数字芯片全局布局方法
Visual-based reinforcement learning for digital chip global placement
摘要
Abstract
In the back-end design of digital chips,it needs to consider both wire length and legalisation during global place-ment.Global placement represents a combinatorial optimization problem.Traditional annealing algorithms or genetic algorithms consume a significant amount of time and are susceptible to entering local optima.Current reinforcement learning solutions sel-dom leverage the overall visual information of the placement.Therefore,this paper proposed a reinforcement learning method that incorporated visual information to attain end-to-end global placement.During the global placement,it mapped the circuit netlist information into multiple image-level features,and utilized CNN and GCN to merge the image features with the netlist informa-tion.It employed a complete set of strategy networks and value networks to conduct comprehensive analysis and optimization of the global placement.Experiments on the ISPD2005 benchmark circuit demonstrate that the designed networks accelerate the convergence speed by approximately 7 times,reduce the placement wire length by 10％to 32％,and achieve a 0％overlap rate.This approach offers an efficient and rational solution for the global placement task of digital chips.关键词
全局布局/深度强化学习/计算机视觉/图卷积神经网络/数字芯片Key words
global placement/deep reinforcement learning/computer vision/graph convolutional neural network/digital chip分类
信息技术与安全科学引用本文复制引用
徐樊丰,仝明磊..基于视觉强化学习的数字芯片全局布局方法[J].计算机应用研究,2024,41(4):1270-1274,5.基金项目
国家自然科学基金资助项目(62105196) （62105196）
