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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 4
1270-1274

Visual-based reinforcement learning for digital chip global placement

Xu Fanfeng
Tong Minglei
School of Electronics & Information Engineering, Shanghai University of Electric Power, Shanghai 201306, China
DOI: 10.19734/j.issn.1001-3695.2023.08.0385

Abstract

In the back-end design of digital chips, it needs to consider both wire length and legalisation during global placement. 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 seldom 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 information. 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.

Foundation Support

国家自然科学基金资助项目(62105196)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.08.0385
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 4
Section: Technology of Graphic & Image
Pages: 1270-1274
Serial Number: 1001-3695(2024)04-046-1270-05

Publish History

[2023-11-01] Accepted Paper
[2024-04-05] Printed Article

Cite This Article

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. (Xu Fanfeng, Tong Minglei. Visual-based reinforcement learning for digital chip global placement [J]. Application Research of Computers, 2024, 41 (4): 1270-1274. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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