6 citations · 10 across the 6 of their papers we have counts for
15 papers
HySpecPro: Scalable Hypergraph Partitioning via Spectral Projection Optimization
Rongjian Liang, Zhuo Feng, Haoxing Ren
Modern VLSI designs comprise tens of billions of components, making scalable hypergraph partitioning critical for parallel and hierarchical optimization. Although multilevel partit…
AUTOGATE: Automated Clock Gating via Toggling-Aware LLM-based RTL Rewriting
Yiting Wang, Chenhui Deng, Chia-Tung Ho +6
Fine-grain clock gating (FGCG) is among the most effective techniques for reducing dynamic power, yet current FGCG optimization flows remain largely manual. Recent LLM-based RTL op…
SF-SGL: Solver-Free Spectral Graph Learning from Linear Measurements
Ying Zhang, Zhiqiang Zhao, Zhuo Feng
This work introduces a highly-scalable spectral graph densification framework (SGL) for learning resistor networks with linear measurements, such as node voltages and currents. We…
HyperEF: Spectral Hypergraph Coarsening by Effective-Resistance Clustering
Ali Aghdaei, Zhuo Feng
This paper introduces a scalable algorithmic framework (HyperEF) for spectral coarsening (decomposition) of large-scale hypergraphs by exploiting hyperedge effective resistances. M…
HyperSF: Spectral Hypergraph Coarsening via Flow-based Local Clustering
Ali Aghdaei, Zhiqiang Zhao, Zhuo Feng
Hypergraphs allow modeling problems with multi-way high-order relationships. However, the computational cost of most existing hypergraph-based algorithms can be heavily dependent u…
SGL: Spectral Graph Learning from Measurements
Zhuo Feng
This work introduces a highly scalable spectral graph densification framework for learning resistor networks with linear measurements, such as node voltages and currents. We prove…