Physics-Informed Graph Neural Network (PI-GNN)

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• Published Subscription Review Article 28 Jul 2026

  A Physics-Informed Graph Neural Network Framework for Real- Time Thermal-Aware Fault Prediction and Adaptive Power Optimization in Heterogeneous System-on-Chip Architectures

  By Bibhu Prasad Ganthia, Subash Ranjan Kabat

  Abstract: 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. However, increasing integration density and workload diversity introduce severe thermal hotspots, accelerated device degradation, and unexpected hardware faults that adversely affect system reliability and energy efficiency. This study proposes a Physics-Informed Graph Neural Network (PI-GNN) framework for real-time thermal- aware fault prediction and adaptive …

  Published in Journal of VLSI Design Tools and Technology · Vol. 16, Issue 2, 2026 Read article →