Comparative Performance Study of Intelligent Edge Devices

Edge computing offers a promising solution to the latency issues inherent in centralized cloud processing, particularly for industrial Internet of Things (IIoT) applications. However, the limited computational capabilities of edge devices pose challenges to optimal artificial intelligence (AI) workload performance. This study provides a comparative performance analysis of several edge devices, focusing on evaluating the impact of hardware accelerators like graphics processing units (GPUs) on AI application processing. We employ YOLOv8, a popular object detection model, to evaluate five tasks―image classification, object detection, pose estimation, instance segmentation, and oriented bounding box detection―by measuring job completion time (JCT), GPU utilization, and memory usage. Our findings indicate that expensive high-end devices do not always provide a proportionate performance boost, with mid-range devices frequently offering comparable inference performance for less computationally demanding tasks. These results underscore the need for a careful balance between hardware specifications and application requirements to achieve efficient and cost-effective AI deployment. Additionally, we observe that multi-threading does not consistently yield performance improvements on edge devices due to Python’ s Global Interpreter Lock (GIL) overhead. This limitation highlights the need for innovative solutions, such as simultaneous task management and GPU scheduling, to improve parallelism and optimize resource utilization in edge environments.