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UID:dac_DAC 2026_sess317_RESEARCH2694@linklings.com
SUMMARY:HySpecPro: Scalable Hypergraph Partitioning via Spectral Projectio
 n Optimization
DESCRIPTION:Rongjian Liang (NVIDIA), Zhuo Feng (Stevens Institute of Techn
 ology), and Haoxing Ren (NVIDIA)\n\nModern VLSI designs contain tens of bi
 llions of components, making scalable hypergraph partitioning essential fo
 r parallel or hierarchical optimization. Although the multilevel partition
 ing paradigm remains effective, coarsening can distort structural informat
 ion—especially in hypergraphs with many high-degree hyperedges—leading to 
 substantial refinement overhead and limited scalability. Recent works inco
 rporate spectral information, but only heuristically and without directly 
 targeting the partitioning objective or enforcing constraints, leaving ref
 inement to recover quality. We introduce HySpecPro, a single-level hypergr
 aph partitioner that performs end-to-end optimization in a spectral embedd
 ing space. HySpecPro constructs embeddings from a bipartite Laplacian and 
 performs efficient projection-based search, supported by a fully GPU-accel
 erated implementation. Experiments show that HySpecPro delivers cut qualit
 y comparable to state-of-the-art multilevel methods while scaling linearly
  with the total hyperedge degree.\n\nTrack: EDA7-II. Physical Design and V
 erification, Design\n\nSession Chairs: Christian Pilato (Politecnico di Mi
 lano); Mohsen Imani (University of California, Irvine); Flavio Ponzina (Sa
 n Diego State University); Amin Farshidi (Cadence Design Systems, Inc.); Y
 onggan Fu (Georgia Institute of Technology, NVIDIA); Dimitrios Tsaras (Hua
 wei, Hong Kong University of Science and Technology (HKUST)); Anh Nguyen (
 Fujitsu Limited); Ruiyang Qin (Villanova University); Jiliang Zhang (Hunan
  University); Meng Li (Peking University); and Rassul Bairamkulov (Advance
 d Micro Devices (AMD))\n\n
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