G-SpNN: GPU-Accelerated Passivity Enforcement for S-Parameter Modeling with Neural Networks
The increasing complexity of high-frequency circuits calls for efficient and accurate passive macromodeling techniques. Existing passivity enforcement methods, including those in commercial tools, often encounter convergence issues or compromise accuracy. The Domain-Alternated Optimization (DAO) fra...
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| Veröffentlicht in: | 2025 62nd ACM/IEEE Design Automation Conference (DAC) S. 1 - 7 |
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22.06.2025
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| Abstract | The increasing complexity of high-frequency circuits calls for efficient and accurate passive macromodeling techniques. Existing passivity enforcement methods, including those in commercial tools, often encounter convergence issues or compromise accuracy. The Domain-Alternated Optimization (DAO) framework seeks to restore accuracy through an additional optimization step but is hampered by high memory consumption and slow convergence, particularly for large-scale problems. This paper presents G-SpNN, a novel GPU-accelerated framework that recasts the passivity-enforced macromodeling problem as a neural network training task. This approach significantly enhances both the speed and scalability of passivity enforcement. Experimental results show that G-SpNN achieves an average speedup of 7.63 \times in convergence compared to DAO, while reducing memory usage by two orders of magnitude. This enables G-SpNN to handle complex, high-port-count circuits with greater accuracy and efficiency, paving the way for robust high-frequency circuit simulations. |
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| AbstractList | The increasing complexity of high-frequency circuits calls for efficient and accurate passive macromodeling techniques. Existing passivity enforcement methods, including those in commercial tools, often encounter convergence issues or compromise accuracy. The Domain-Alternated Optimization (DAO) framework seeks to restore accuracy through an additional optimization step but is hampered by high memory consumption and slow convergence, particularly for large-scale problems. This paper presents G-SpNN, a novel GPU-accelerated framework that recasts the passivity-enforced macromodeling problem as a neural network training task. This approach significantly enhances both the speed and scalability of passivity enforcement. Experimental results show that G-SpNN achieves an average speedup of 7.63 \times in convergence compared to DAO, while reducing memory usage by two orders of magnitude. This enables G-SpNN to handle complex, high-port-count circuits with greater accuracy and efficiency, paving the way for robust high-frequency circuit simulations. |
| Author | Wu, Xiao Jin, Zhou Wang, Tianshi Ye, Zuochang Lin, Yibo Zeng, Lijie Sun, Jiatai Niu, Dan |
| Author_xml | – sequence: 1 givenname: Lijie surname: Zeng fullname: Zeng, Lijie email: z.jin@zju.edu.cn organization: China University of Petroleum-Beijing,SSSLab,China – sequence: 2 givenname: Jiatai surname: Sun fullname: Sun, Jiatai organization: China University of Petroleum-Beijing,SSSLab,China – sequence: 3 givenname: Xiao surname: Wu fullname: Wu, Xiao organization: Huada Empyrean Software Co. Ltd,China – sequence: 4 givenname: Dan surname: Niu fullname: Niu, Dan organization: Southeast University,School of Automation,China – sequence: 5 givenname: Tianshi surname: Wang fullname: Wang, Tianshi organization: HiSilicon Technologies Co. Ltd – sequence: 6 givenname: Yibo surname: Lin fullname: Lin, Yibo organization: Peking University,School of Integrated Circuits,Beijing – sequence: 7 givenname: Zuochang surname: Ye fullname: Ye, Zuochang organization: Tsinghua University,School of Integrated Circuits – sequence: 8 givenname: Zhou surname: Jin fullname: Jin, Zhou organization: Zhejiang University,College of Integrated Circuits,China |
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| Snippet | The increasing complexity of high-frequency circuits calls for efficient and accurate passive macromodeling techniques. Existing passivity enforcement methods,... |
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| SubjectTerms | Accuracy Convergence GPU Acceleration Graphics processing units Integrated circuit modeling Memory management Neural Network Neural networks Optimization Passivity Enforcement S-Parameter Macromodeling Scalability Scattering parameters Training |
| Title | G-SpNN: GPU-Accelerated Passivity Enforcement for S-Parameter Modeling with Neural Networks |
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