Paper accepted @ IEEE AGCS 2026

Konferenz: IEEE AGCS 2026 (Symposium on Edge intelligence, Trustworthy and Decentralized Artificial Intelligence- iEdge)

Wo: 27-30 October 2026, Paris, France.

Titel: A Multi-Node Performance Evaluation of Shared Edge AI Inference

Autoren: Raphael Walcher, Dragi Kimovski, Kurt Horvath

Abstract: 

Edge-based Artificial Intelligence (AI) enables latency-sensitive applications by moving inference closer to data sources. While previous studies have demonstrated the feasibility of inference offloading for individual edge devices, the scalability of shared inference resources under concurrent workloads remains insufficiently understood. This paper experimentally evaluates a multi-node Edge AI architecture in which three roadside units (RSUs) concurrently offload object detection sharing a Zone Processor (ZP) connected through Wi-Fi 6E.

A comprehensive experimental campaign comprising 648 parameter configurations investigates the impact of AI model complexity, capture frame rate, image encoding, and multi-node contention on throughput, latency, and system scalability.

The results demonstrate that inference offloading consistently outperforms local execution on resource-constrained RSUs, reducing end-to-end latency by up to two seconds for computationally demanding models. Throughput analysis further reveals model-dependent computational saturation limits of approximately 18 FPS, 9 FPS, and 3 FPS aggregate throughput for YOLOv5n, YOLOv5s, and YOLOv5m, respectively. Latency component analysis shows that inference execution and the resulting queue waiting account for more than 95% of the end-to-end latency, whereas transmission contributes only a small and nearly constant fraction. These findings establish model complexity and shared inference capacity, rather than communication latency, as the dominant factors governing the performance and scalability of shared Edge AI deployments.