Graph Neural Networks for Efficient Clock Tree Synthesis Optimization in Complex SoC Designs
- 1 University of Southern California, Los Angeles, USA
- 2 University of Texas at Dallas, Richardson, USA
- 3 University of Southern California, Los Angeles, USA
- 4 Carnegie Mellon University, USA
* Author to whom correspondence should be addressed.
Abstract
This paper presents a novel graph neural network (GNN) based framework for efficient clock tree synthesis (CTS) optimization in complex System-on-Chip designs. As technology nodes advance to 5nm and below, traditional CTS methodologies face significant challenges in optimizing power, performance, and skew metrics while managing exponentially growing design complexity. We propose a specialized GNN architecture incorporating bidirectional message passing mechanisms and attention components to effectively capture critical clock network characteristics. The framework implements a multi-objective optimization approach that simultaneously addresses power consumption, insertion delay, and clock skew constraints through reinforcement learning techniques. Our hybrid methodology integrates GNN-based predictions with conventional CTS algorithms, achieving a synergistic workflow that preserves design rule compliance while enhancing optimization capabilities. Experimental evaluation across multiple benchmark circuits and industrial SoC designs demonstrates average reductions of 8.7% in clock power, 6.3% in maximum skew, and 1.8% in insertion delay compared to state-of-the-art commercial tools, while simultaneously reducing runtime by 56.2%. The performance advantages scale favorably with increasing design complexity, showing sublinear computational growth compared to the superlinear scaling of traditional methods. The framework demonstrates robust performance across diverse application domains including mobile processors, automotive controllers, and AI accelerators, validating its practical applicability in advanced technology nodes.
Keywords
Clock Tree Synthesis, Graph Neural Networks, Physical Design Optimization, Machine Learning for EDA
[1]. Lee, P. Y., & Chen, T. C. (2024, April). AI-Driven Innovations in IC Designs: From Planning to Implementation. In 2024 International VLSI Symposium on Technology, Systems and Applications (VLSI TSA) (pp. 1-2). IEEE.
[2]. Jain, A., Das, P., Acharyya, A., & Rakesh, M. B. (2024, May). ANN-based Accurate and Fast Post-Route QoR Data Prediction Methodology from Pre-Clock Tree Synthesis by Skipping CTS and Routing. In 2024 IEEE International Symposium on Circuits and Systems (ISCAS) (pp. 1-5). IEEE.
[3]. Levy, A., Walston, J., Samanta, S., Raina, P., & Diamantidis, S. (2024, April). FastPASE: An AI-Driven Fast PPA Speculation Engine for RTL Design Space Optimization. In 2024 25th International Symposium on Quality Electronic Design (ISQED) (pp. 1-8). IEEE.
[4]. Ren, H., Khailany, B., Fojtik, M., & Zhang, Y. (2022). Machine learning and algorithms: Let us team up for EDA. IEEE Design & Test, 40(1), 70-76.
[5]. Wen, J., Kang, J., Niyato, D., Zhang, Y., & Mao, S. (2024). Sustainable Diffusion-based Incentive Mechanism for Generative AI-driven Digital Twins in Industrial Cyber-Physical Systems. IEEE Transactions on Industrial Cyber-Physical Systems.
[6]. Yu, P., Xu, Z., Wang, J., & Xu, X. (2025). The Application of Large Language Models in Recommendation Systems. arXiv preprint arXiv:2501.02178.
[7]. Weng, J., & Jiang, X. (2024). Research on Movement Fluidity Assessment for Professional Dancers Based on Artificial Intelligence Technology. Artificial Intelligence and Machine Learning Review, 5(4), 41-54.
[8]. Jiang, C., Jia, G., & Hu, C. (2024). AI-Driven Cultural Sensitivity Analysis for Game Localization: A Case Study of Player Feedback in East Asian Markets. Artificial Intelligence and Machine Learning Review, 5(4), 26-40.
[9]. Ma, D. (2024). AI-Driven Optimization of Intergenerational Community Services: An Empirical Analysis of Elderly Care Communities in Los Angeles. Artificial Intelligence and Machine Learning Review, 5(4), 10-25.
[10]. Ma, D., & Ling, Z. (2024). Optimization of Nursing Staff Allocation in Elderly Care Institutions: A Time Series Data Analysis Approach. Annals of Applied Sciences, 5(1).
[11]. Zheng, S., Zhang, Y., & Chen, Y. (2024). Leveraging Financial Sentiment Analysis for Detecting Abnormal Stock Market Volatility: An Evidence-Based Approach from Social Media Data. Academia Nexus Journal, 3(3).
[12]. Sun, J., Zhou, S., Zhan, X., & Wu, J. (2024). Enhancing Supply Chain Efficiency with Time Series Analysis and Deep Learning Techniques.
[13]. Wang, P., Varvello, M., Ni, C., Yu, R., & Kuzmanovic, A. (2021, May). Web-lego: trading content strictness for faster webpages. In IEEE INFOCOM 2021-IEEE Conference on Computer Communications (pp. 1-10). IEEE.
[14]. Rao, G., Trinh, T. K., Chen, Y., Shu, M., & Zheng, S. (2024). Jump Prediction in Systemically Important Financial Institutions' CDS Prices. Spectrum of Research, 4(2).
[15]. Fan, J., Zhu, Y., & Zhang, Y. (2024). Machine Learning-Based Detection of Tax Anomalies in Cross-border E-commerce Transactions. Academia Nexus Journal, 3(3).
Cite this article
Wu,J.;Ni,C.;Wang,H.;Chen,J. (2025). Graph Neural Networks for Efficient Clock Tree Synthesis Optimization in Complex SoC Designs. Applied and Computational Engineering,150,101-111.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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