Edge AI Acceleration for Real-Time Graph Neural Network Inference

Abstract

Real-time graph neural network inference at the edge has emerged as a critical systems challenge because graph-structured data increasingly mediates mobility, industrial monitoring, health sensing, communication networks, and urban infrastructure. Unlike conventional image or sequence inference, graph neural networks exhibit irregular memory access, dynamic neighborhood expansion, sparse feature aggregation, and strong dependencies between topology and computation. These properties complicate the deployment of low-latency inference on resource-constrained devices, where energy, thermal headroom, memory bandwidth, and intermittent connectivity are limited. This paper examines edge artificial intelligence acceleration for real-time graph neural network inference from a system-level perspective. It argues that meaningful acceleration requires co-design across hardware architecture, runtime orchestration, graph partitioning, model compression, deployment governance, and socio-technical policy. The discussion first clarifies the computational and infrastructural foundations of edge graph inference. It then analyzes architectural trade-offs among mobile graphics processors, neural processing units, field-programmable gate arrays, and application-specific accelerators. Subsequent sections address runtime infrastructure, distributed deployment across edge-cloud continua, robustness under noisy and adversarial conditions, fairness in graph-mediated decisions, sustainability, and policy implications. The paper concludes that real-time graph inference at the edge is not merely a kernel optimization problem but an institutional and infrastructural problem requiring adaptive acceleration, transparent evaluation, and accountable governance.