Agile hardware development requires fast and accurate circuit quality evaluation from early design stages. Existing work of high-level synthesis (HLS) performance prediction usually needs extensive feature engineering after the synthesis process. To expedite circuit evaluation from as earlier design stage as possible, we propose a rapid and accurate performance modeling, exploiting the representation power of graph neural networks (GNNs) by representing C/C++ programs as graphs. The contribution of this work is three-fold. First, we build a standard benchmark containing 40k C synthesizable programs, which includes both synthetic programs and three sets of real-world HLS benchmarks. Each program is implemented on FPGA to generate ground-truth performance metrics. Second, we formally formulate the HLS performance prediction problem on graphs, and propose multiple modeling strategies with GNNs that leverage different trade-offs between prediction timeliness (early/late prediction) and accuracy. Third, we further propose a novel hierarchical GNN that does not sacrifice timeliness but largely improves prediction accuracy, significantly outperforming HLS tools. We apply extensive evaluations for both synthetic and unseen real-case programs; our proposed predictor largely outperforms HLS by up to 40X and excels existing predictors by 2X to 5X in terms of resource usage and timing prediction.
GNNX-BENCH: Unravelling the Utility of Perturbation-based GNN Explainers through In-depth Benchmarking
University of California Santa Barbara（加州大学圣巴巴拉分校）；Indian Institute of Technology Delhi（印度理工学院德里分校）；Rice University（莱斯大学）；University of Illinois Chicago（芝加哥大学伊利诺伊分校）
Advancing Heatwave Forecasting via Distribution Informed-Graph Neural Networks (DI-GNNs): Integrating Extreme Value Theory with GNNs
Institute for Resources, Environment and Sustainability, University of British Columbia（不列颠哥伦比亚大学资源、环境与可持续性研究所）；Environment and Climate Change Canada（加拿大环境与气候变化部）；Department of Earth, Ocean and Atmospheric Sciences, University of British Columbia（不列颠哥伦比亚大学地球、海洋与大气科学系）；Department of Forestry, University of British Columbia（不列颠哥伦比亚大学林业系）；Department of Occupational Science and Occupational Therapy, University of British Columbia（不列颠哥伦比亚大学职业科学与职业治疗系）；Department of Space Science, Institute of Space Technology（空间技术研究院空间科学系）；Peaceful Society, Science and Innovation Foundation（和平学会科学与创新基金会）
Benchmarking Graph Neural Networks in Solving Hard Constraint Satisfaction Problems
Department of Computing Sciences, Bocconi University（博科尼大学计算机科学系）；Bocconi Institute for Data Science and Analytics (BIDSA)（博科尼数据科学与分析研究所）；Department of Physics and Astronomy and INFN, University of Florence（佛罗伦萨大学物理与天文学系及意大利国家核物理研究所）；Dipartimento di Fisica, Sapienza Università di Roma（罗马萨皮恩扎大学物理系）；CNR - Nanotec, unità di Roma, P.le Aldo Moro 5, 00185 Rome, Italy（意大利罗马国家科研委员会纳米技术单元，Aldo Moro大道5号，00185罗马，意大利）；Department of Theoretical Physics, Physics Faculty, University of Havana（古巴哈瓦那大学理论物理系）；INFN, sezione di Roma1, P.le Aldo Moro 5, 00185 Rome, Italy（意大利罗马1研究所，Aldo Moro大道5号，00185罗马，意大利）
Nanyang Technological University（南洋理工大学）；University of Cambridge（剑桥大学）；Loyola Marymount University（洛约拉马利蒙特大学）；Mila（米拉人工智能研究所）；University of Montréal（蒙特利尔大学）；National University of Singapore（新加坡国立大学）
Task-Agnostic Graph Neural Network Evaluation via Adversarial Collaboration
Imperial College London（帝国理工学院）；Massachusetts Institute of Technology（麻省理工学院）；Valence Discovery（Valence Discovery公司）；Mila（米拉研究所）；Université de Montréal（蒙特利尔大学）；University of Cambridge（剑桥大学）
Theoretical guarantees for the advantage of GNNs over NNs in generalizing bandlimited functions on Euclidean cubes
Faculty of Mathematics, University of Vienna（维也纳大学数学学院）；Department of Mathematics, Michigan State University（密歇根州立大学数学系）；Department of Statistics and Probability, Michigan State University（密歇根州立大学统计与概率系）
Taxonomy of Benchmarks in Graph Representation Learning
Michigan State University（密歇根州立大学）；Mila（米拉人工智能研究所）；Université de Montréal（蒙特利尔大学）；University of Utah（犹他大学）；ETH Zürich（苏黎世联邦理工学院）；University of California, Los Angeles（加州大学洛杉矶分校）；Helmholtz Munich（亥姆霍兹慕尼黑中心）；TU Munich（慕尼黑工业大学）
B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data
University of Edinburgh（爱丁堡大学）；Jagiellonian University（雅盖隆大学）；Jagiellonian University, Faculty of Mathematics and Computer Science（雅盖隆大学数学与计算机科学学院）；Jagiellonian University, Faculty of Chemistry（雅盖隆大学化学学院）；Ardigen SA（Ardigen公司）
Making Classic GNNs Strong Baselines Across Varying Homophily: A Smoothness-Generalization Perspective
Zhejiang Key Laboratory of Accessible Perception and Intelligent Systems, Zhejiang University（浙江可及感知与智能系统重点实验室，浙江大学）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Software Technology, Zhejiang University（浙江大学软件学院）；China University of Mining and Technology（中国矿业大学）；Department of Computer Science, New York University Shanghai（纽约大学上海分校计算机科学系）
Graph Neural Networks for Interferometer Simulations
College of Creative Studies University of California, Santa Barbara（加州大学圣芭芭拉分校创意研究学院）；Department of Physics & Astronomy University of California, Riverside（加州大学河滨分校物理与天文学系）；Department of Computer Science & Engineering University of California, Riverside（加州大学河滨分校计算机科学与工程系）
Explainable Fuzzy GNNs for Leak Detection in Water Distribution Networks
Jheronimus Academy of Data Science, Eindhoven University of Technology, Eindhoven, The Netherlands University of Bari Aldi Moro, Bari, Italy IHE Institute for Water Education, Delft, The Netherlands University of Cartagena, Cartagena, Spain School of Industrial Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
Dept. of Mathematics, Georgia Institute of Technology, Atlanta, GA, USA Dept. of Computer Science \& Engineering, Texas A \& M University, College Station, TX, USA Dept. of Mathematical Sciences, The University of Texas at Dallas, Richardson, TX, USA
Indian Institute of Information Technology Kottayam (IIITK)（印度信息技术学院科塔亚姆分校（IIITK））；International Institute of Information Technology Hyderabad (IIITH)（印度信息技术学院海得拉巴分校（IIITH））
