CSF: Fixed-Outline Floorplanning Based on the Conjugate Subgradient Algorithm and Assisted by Q-Learning

Meng, Xinyan; Cheng, Huabin; Chen, Yu; Hu, Jianguo; Xu, Ning

doi:10.3390/electronics14244893

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CSF: Fixed-Outline Floorplanning Based on the Conjugate Subgradient Algorithm and Assisted by Q-Learning

by

Xinyan Meng

^1,†,

Huabin Cheng

^2,†

,

Yu Chen

^1,*

,

Jianguo Hu

^3,4,* and

Ning Xu

⁵

¹

School of Mathematics and Statistics, Wuhan University of Technology, Wuhan 430070, China

²

Department of Basic Science, Wuchang Shouyi University, Wuhan 430064, China

³

Guangdong Provincial Key Laboratory of High-End Integrated Circuit Design and Integration Technology, Sun Yat-Sen University, Zhuhai 519082, China

⁴

Shenzhen Research Institute of Sun Yat-Sen University, Shenzhen 528406, China

⁵

School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China

^*

Authors to whom correspondence should be addressed.

^†

These authors contributed equally to this work.

Electronics 2025, 14(24), 4893; https://doi.org/10.3390/electronics14244893

Submission received: 13 November 2025 / Revised: 5 December 2025 / Accepted: 8 December 2025 / Published: 12 December 2025

(This article belongs to the Special Issue AI-Enhanced Mixed-Signal Simulation and EDA for Integrated Circuit Design Using CMOS Technologies)

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Abstract

Analytical floorplanning algorithms are prone to local convergence and struggle to generate high-quality results; therefore, this paper proposes a nonsmooth analytical placement model and develops a Q-learning-assisted conjugate subgradient algorithm (CSAQ) for efficient floorplanning that addresses these issues. By integrating a population-based strategy and an adaptive step size adjustment driven by Q-learning, the CSAQ strikes a balance between exploration and exploitation to avoid suboptimal solutions in fixed-outline floorplanning scenarios. Experimental results on the MCNC and GSRC benchmarks demonstrate that the proposed CSAQ not only effectively solves global placement planning problems but also significantly outperforms existing constraint graph-based legalization methods, as well as the improved variants, in terms of the efficiency of generating legal floorplans. For hard module-only placement scenarios, it exhibits competitive performance compared to the state-of-the-art algorithms.

Keywords: fixed-outline floorplanning; nonsmooth analytic optimization; conjugate subgradient algorithm; Q-learning; very large-scale integration circuit

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MDPI and ACS Style

Meng, X.; Cheng, H.; Chen, Y.; Hu, J.; Xu, N. CSF: Fixed-Outline Floorplanning Based on the Conjugate Subgradient Algorithm and Assisted by Q-Learning. Electronics 2025, 14, 4893. https://doi.org/10.3390/electronics14244893

AMA Style

Meng X, Cheng H, Chen Y, Hu J, Xu N. CSF: Fixed-Outline Floorplanning Based on the Conjugate Subgradient Algorithm and Assisted by Q-Learning. Electronics. 2025; 14(24):4893. https://doi.org/10.3390/electronics14244893

Chicago/Turabian Style

Meng, Xinyan, Huabin Cheng, Yu Chen, Jianguo Hu, and Ning Xu. 2025. "CSF: Fixed-Outline Floorplanning Based on the Conjugate Subgradient Algorithm and Assisted by Q-Learning" Electronics 14, no. 24: 4893. https://doi.org/10.3390/electronics14244893

APA Style

Meng, X., Cheng, H., Chen, Y., Hu, J., & Xu, N. (2025). CSF: Fixed-Outline Floorplanning Based on the Conjugate Subgradient Algorithm and Assisted by Q-Learning. Electronics, 14(24), 4893. https://doi.org/10.3390/electronics14244893

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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CSF: Fixed-Outline Floorplanning Based on the Conjugate Subgradient Algorithm and Assisted by Q-Learning

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