Hardware-Aware Graph Neural Network Optimization for Scalable Integrated Circuit Design and Verification

Abstract

The design and verification of modern integrated circuits increasingly depend on computational methods that can reason over large, heterogeneous graphs representing netlists, timing paths, physical layout, and verification constraints. Graph neural networks have emerged as a promising class of models for learning such representations, yet their practical deployment in electronic design automation remains constrained by hardware efficiency, data movement, model robustness, and verification-grade reliability. This paper examines hardware-aware graph neural network optimization as a system-level problem rather than a narrow model-compression task. It argues that scalable circuit design and verification require coordinated choices across graph representation, accelerator architecture, memory hierarchy, partitioning strategy, distributed training infrastructure, and governance mechanisms. The discussion addresses structural trade-offs between accuracy, latency, throughput, energy consumption, and interpretability in design workflows where correctness and reproducibility are non-negotiable. It further considers deployment in cloud and edge environments, the sustainability implications of large-scale training and inference, and the fairness and policy questions that arise when proprietary electronic design automation infrastructures shape access to advanced semiconductor capabilities. Through cross-domain comparisons with machine learning systems, datacenter computing, time-sensitive networking, and safety engineering, the paper develops a forward-looking perspective on co-design, verification, and institutional accountability. The central claim is that hardware-aware graph learning can become a durable foundation for scalable circuit design only if optimization is understood as a socio-technical practice encompassing architecture, infrastructure, robustness, and governance.