Embracing Graph Neural Networks for Hardware Security
Lilas Alrahis, Satwik Patnaik, Muhammad Shafique, Ozgur Sinanoglu
2022Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design19 citationsDOI
Abstract
Graph neural networks (GNNs) have attracted increasing attention due to their superior performance in deep learning on graph-structured data. GNNs have succeeded across various domains such as social networks, chemistry, and electronic design automation (EDA). Electronic circuits have a long history of being represented as graphs, and to no surprise, GNNs have demonstrated state-of-the-art performance in solving various EDA tasks. More importantly, GNNs are now employed to address several hardware security problems, such as detecting intellectual property (IP) piracy and hardware Trojans (HTs), to name a few.
Topics & Concepts
Computer scienceSurpriseGraphElectronic design automationArtificial neural networkDeep learningTheoretical computer scienceArtificial intelligenceComputer engineeringEmbedded systemPsychologySocial psychologyPhysical Unclonable Functions (PUFs) and Hardware SecurityIntegrated Circuits and Semiconductor Failure AnalysisNeuroscience and Neural Engineering