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DTSTAMP:20260402T024534Z
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DTSTART;TZID=America/Los_Angeles:20250623T111500
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UID:dac_DAC 2025_sess139_RESEARCH010@linklings.com
SUMMARY:G-SpNN: GPU-Accelerated Passivity Enforcement for S-Parameter Mode
 ling with Neural Networks
DESCRIPTION:Lijie Zeng and Jiatai Sun (China University of Petroleum, Beij
 ing); Xiao Wu (Huada Empyrean Software Co. Ltd); Dan Niu (Southeast Univer
 sity); Tianshi Wang (University of California, Berkeley); Yibo Lin (Peking
  University); Zuochang Ye (Tsinghua University); and Zhou Jin (China Unive
 rsity of Petroleum, Beijing)\n\nThe increasing complexity of high-frequenc
 y circuits calls for efficient and accurate passive macromodeling techniqu
 es.\nExisting passivity enforcement methods, including those in commercial
  tools, often encounter convergence issues or compromise accuracy.\nThe Do
 main-Alternated Optimization (DAO) framework seeks to restore accuracy thr
 ough an additional optimization step but is hampered by high memory consum
 ption and slow convergence, particularly for large-scale problems.  \nThis
  paper presents \ours, a novel GPU-accelerated framework that recasts the 
 passivity-enforced macromodeling problem as a neural network training task
 .\nThis approach significantly enhances both the speed and scalability of 
 passivity enforcement.\nExperimental results show that \ours achieves an a
 verage speedup of 7.63$\times$ in convergence compared to DAO, while reduc
 ing memory usage by two orders of magnitude.\nThis enables \ours to handle
  complex, high-port-count circuits with greater accuracy and efficiency, p
 aving the way for robust high-frequency circuit simulations.\n\nTopics: ED
 A\n\nTracks: EDA6: Analog CAD, Simulation, Verification and Test\n\nSessio
 n Chairs: Arindam Basu (School of Computer Science and Engineering, Nanyan
 g Technological University) and Markus Olbrich (Leibniz University Hannove
 r)\n\n
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