PERFORMANCE BENCHMARKING OF EMBEDDED EDGE DEVICES FOR VARIOUS FACE RECOGNITION MODELS

Summary: Artificial Intelligence (AI) models are increasingly pivotal in enabling face recognition across various fields, from educational and research settings to public spaces. Effective deployment of these models requires high-performance hardware, such as RTX graphics cards or embedded edge devices like Nvidia's AGX Orin and Jetson Nano. This paper pre-sents a comprehensive benchmarking study comparing the performance of these two devices, representing high and low-power edge computing options, using two face recognition models: ResNet and MobileNet. The benchmarking process assesses each model across two different input sizes deployed on both devices with varied configurations, inclu-ding CPU thread allocation and GPU power distribution within contai-nerized environments. Performance metrics such as inference time, GPU utilization, memory usage, and CPU load are analyzed to determine each device's suitability and efficiency. Additionally, model-specific parameters, including FLOPS, parameter count, and memory footprint, are examined to provide for an in-depth comparison. This paper pre-sents detailed results and analyses of these performance indicators.