14 citations · 52 across the 15 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…
SPADE: A Spectral Method for Black-Box Adversarial Robustness Evaluation
Wuxinlin Cheng, Chenhui Deng, Zhiqiang Zhao +3
A black-box spectral method is introduced for evaluating the adversarial robustness of a given machine learning (ML) model. Our approach, named SPADE, exploits bijective distance m…
SF-GRASS: Solver-Free Graph Spectral Sparsification
Ying Zhang, Zhiqiang Zhao, Zhuo Feng
Recent spectral graph sparsification techniques have shown promising performance in accelerating many numerical and graph algorithms, such as iterative methods for solving large sp…
GRASPEL: Graph Spectral Learning at Scale
Yongyu Wang, Zhiqiang Zhao, Zhuo Feng
Learning meaningful graphs from data plays important roles in many data mining and machine learning tasks, such as data representation and analysis, dimension reduction, data clust…
GRASS: Graph Spectral Sparsification Leveraging Scalable Spectral Perturbation Analysis
Zhuo Feng
Spectral graph sparsification aims to find ultra-sparse subgraphs whose Laplacian matrix can well approximate the original Laplacian eigenvalues and eigenvectors. In recent years,…
GraphZoom: A multi-level spectral approach for accurate and scalable graph embedding
Chenhui Deng, Zhiqiang Zhao, Yongyu Wang +2
Graph embedding techniques have been increasingly deployed in a multitude of different applications that involve learning on non-Euclidean data. However, existing graph embedding m…
Nearly-Linear Time Spectral Graph Reduction for Scalable Graph Partitioning and Data Visualization
Zhiqiang Zhao, Yongyu Wang, Zhuo Feng
This paper proposes a scalable algorithmic framework for spectral reduction of large undirected graphs. The proposed method allows computing much smaller graphs while preserving th…
A Unified Approach to Scalable Spectral Sparsification of Directed Graphs
Ying Zhang, Zhiqiang Zhao, Zhuo Feng
Recent spectral graph sparsification research allows constructing nearly-linear-sized subgraphs that can well preserve the spectral (structural) properties of the original graph, s…
Similarity-Aware Spectral Sparsification by Edge Filtering
Zhuo Feng
In recent years, spectral graph sparsification techniques that can compute ultra-sparse graph proxies have been extensively studied for accelerating various numerical and graph-rel…
Towards Scalable Spectral Clustering via Spectrum-Preserving Sparsification
Yongyu Wang, Zhuo Feng
The eigendeomposition of nearest-neighbor (NN) graph Laplacian matrices is the main computational bottleneck in spectral clustering. In this work, we introduce a highly-scalable, s…