Graph Neural Networks for Integrated Circuit Design, Reliability, and Security: Survey and Tool
Ziad El Sayed, Zeng Wang, Hana Selmani, Johann Knechtel, Ozgur Sinanoglu, Lilas Alrahis
University College London New York University Brooklyn College New York University Abu Dhabi
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摘要与影响
Graph neural networks (GNNs) have significantly advanced learning and predictive tasks in many domains like social networks and biology. Given the inherent graph structure of integrated circuits (ICs), GNNs have also shown strong results for various IC-related tasks. Here, we review GNN methodologies across three key areas for ICs: electronic design automation (EDA), reliability, and hardware security. We introduce a comprehensive taxonomy and survey, covering various tasks and their solutions by GNNs in depth. We also outline key challenges like scalability and EDA tool integration. Finally, we present GNN4CIRCUITS, an open-source tool for plug-and-play GNN integration for various IC tasks.
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计算机 / AIPhysical Unclonable Functions (PUFs) and Hardware Security
Integrated Circuits and Semiconductor Failure Analysis · Advanced Memory and Neural Computing
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