A Physics-Informed Graph Neural Network Framework for Real- Time Thermal-Aware Fault Prediction and Adaptive Power Optimization in Heterogeneous System-on-Chip Architectures
Heterogeneous System-on-Chip (SoC) architectures are increasingly adopted in edge computing, artificial intelligence, autonomous systems, and high-performance embedded platforms due to their superior computational efficiency and flexibility.
Physics-Informed Graph Neural Network (PI-GNN); Heterogeneous System-on-Chip (SoC); Thermal-Aware Fault Prediction; Adaptive Power Optimization; Electronic Design Automation (EDA); Edge Computing.