Circuit design is complicated and requires extensive domain-specific expertise. One major obstacle stuck on the way to hardware agile development is the considerably time-consuming process of accurate circuit quality evaluation. To significantly expedite the circuit evaluation during the translation from behavioral languages to circuit designs, we formulate it as a Program-to-Circuit problem, aiming to exploit the representation power of graph neural networks (GNNs) by representing C/C++ programs as graphs. The goal of this work is four-fold. First, we build a standard benchmark containing 40k C/C++ programs, each of which is translated to a circuit design with actual hardware quality metrics, aiming to facilitate the development of effective GNNs targeting this high-demand circuit design area. Second, 14 state-of-the-art GNN models are analyzed on the Program-to-Circuit problem. We identify key design challenges of this problem, which should be carefully handled but not yet solved by existing GNNs. The goal is to provide domain-specific knowledge for designing GNNs with suitable inductive biases. Third, we discuss three sets of real-world benchmarks for GNN generalization evaluation, and analyze the performance gap between standard programs and the real-case ones. The goal is to enable transfer learning from limited training data to real-world large-scale circuit design problems. Fourth, the Program-to-Circuit problem is a representative within the Program-to-X framework, a set of program-based analysis problems with various downstream tasks. The in-depth understanding of strength and weaknesses in applying GNNs on Program-to-Circuit could largely benefit the entire family of Program-to-X. Pioneering in this direction, we expect more GNN endeavors to revolutionize this high-demand Program-to-Circuit problem and to enrich the expressiveness of GNNs on programs.
Graph Neural Networks for Natural Language Processing: A Survey
Pinterest（拼趣）；Rensselaer Polytechnic Institute（伦斯勒理工学院）；Zhejiang University（浙江大学）；IBM T.J. Watson Research Center（IBM托马斯·J·沃森研究中心）；Central China Normal University（华中师范大学）；Nanjing University（南京大学）；Simon Fraser University（西蒙弗雷泽大学）
Perturb More, Trap More: Understanding Behaviors of Graph Neural Networks
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology（健康大数据智能分析技术联合工程研究中心）
Graph Neural Networks for Charged Particle Tracking on FPGAs
University of Pennsylvania（宾夕法尼亚大学）；University of California San Diego（加州大学圣地亚哥分校）；National Yang Ming Chiao Tung University（国立阳明交通大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Princeton University（普林斯顿大学）；University of Washington（华盛顿大学）
VDW-GNNs: Vector diffusion wavelets for geometric graph neural networks
Program in Computing, Boise State University, Boise, Idaho, USA（博伊西州立大学计算项目）；Department of Mathematics, UCLA, Los Angeles, CA, USA（洛杉矶大学数学系）；Department of Computer Science, Yale University, New Haven, CT, USA（耶鲁大学计算机科学系）；Department of Genetics, Yale University, New Haven, CT, USA（耶鲁大学遗传学系）；Department of Mathematics, Boise State University, Boise, Idaho, USA（博伊西州立大学数学系）
From Images to Connections: Can DQN with GNNs learn the Strategic Game of Hex?
TU Darmstadt（达姆施塔特工业大学）；Hessian Center for Artificial Intelligence (hessian.AI)（黑森人工智能中心）；Centre for Cognitive Science, TU Darmstadt（达姆施塔特工业大学认知科学中心）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）
Verifying Quantized Graph Neural Networks is PSPACE-complete
Technical University of Kaiserslautern（凯泽斯劳滕技术大学）；ENS de Lyon, CNRS, Université Claude Bernard Lyon 1, Inria, LIP, UMR 5668（里昂高等师范学院、国家科学研究中心、克劳德·贝尔纳大学 Lyon 1、法国国家信息与自动化研究所、LIP、UMR 5668）；Gran Sasso Science Institute (GSSI)（格兰·萨索科学研究所）
Inference in Probabilistic Graphical Models by Graph Neural Networks
Hanyang University（汉阳大学）；Baylor College of Medicine（贝勒医学院）；Rice University（莱斯大学）；University of Toronto（多伦多大学）；Uber ATG Toronto（优步先进技术集团多伦多分部）；Vector Institute（向量研究所）；Canadian Institute for Advanced Research（加拿大高等研究院）
Jointly Attacking Graph Neural Network and its Explanations
The Hong Kong Polytechnic University（香港理工大学）；Michigan State University（密歇根州立大学）；The Pennsylvania State University（宾夕法尼亚州立大学）；City University of Hong Kong（香港城市大学）；IBM T.J. Watson（IBM托马斯·J·沃森研究中心）
A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems
Université Paris-Saclay（巴黎-萨克雷大学）；CentraleSupélec（中央高等电力学院）；Inria（法国国家信息与自动化研究所）；Qualcomm AI Research（高通人工智能研究部）；Mila（米拉计算科学研究所）；University of Cambridge（剑桥大学）；Université de Montréal（蒙特利尔大学）；Intel Labs（英特尔实验室）；Qualcomm Technologies, Inc.（高通技术公司）；University of Oxford（牛津大学）
Parallelizing Node-Level Explainability in Graph Neural Networks
Ericsson Cognitive Labs（爱立信认知实验室）；Institute for Research in Technology (IIT), ICAI School of Engineering, Comillas Pontifical University（技术研究所（IIT）、ICAI工程学院、科利马大学）
Neural Networks for Local Search and Crossover in Vehicle Routing: A Possible Overkill?
Pontifical Catholic University of Rio de Janeiro（里约热内卢天主教大学）；Jacobs Technion-Cornell Institute（雅各布斯泰克尼昂-康奈尔研究所）；Cornell Tech（康奈尔理工学院）；Technion - IIT（以色列理工学院）
Learning cosmology and clustering with cosmic graphs
Computer Vision Center - Universitat Autònoma de Barcelona（巴塞罗那自治大学计算机视觉中心）；Center for Computational Astrophysics, Flatiron Institute（熨斗研究所计算天体物理学中心）；Princeton University（普林斯顿大学）
Adapting to Heterophilic Graph Data with Structure-Guided Neighbor Discovery
Department of Signal Theory and Communications, King Juan Carlos University（信号理论与通信系，国王胡安·卡洛斯大学）；Department of Electrical and Computer Engineering, Rice University（电气与计算机工程系，里奇大学）
Equivariant Spatio-Temporal Attentive Graph Networks to Simulate Physical Dynamics
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；North Carolina State University（北卡罗来纳州立大学）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院）；Tencent AI Lab（腾讯AI实验室）
