Evaluation of AI and Video Computing Applications on Multiple Heterogeneous Architectures

This paper evaluates an AI video surveillance application on diverse high-performance computing (HPC) architectures. AI-powered video surveillance has emerged as a vital tool for security and monitoring, relying on hardware infrastructure for efficient processing. We present a benchmark of an AI application based on the YOLO object dection framework to track downed pepole in critical scenarios. This study investigates the impact of different architectural designs, including CPUs and GPUs on video analysis performance. Evaluation metrics encompass computational speed, power consumption and resource utilisation.