An Evaluation of Modern Accelerator-Based Edge Devices for Object Detection Applications

Abstract

Edge AI is one of the newly emerged application domains where networked IoT (Internet of Things) devices are deployed to perform AI computations at the edge of the cloud environments. Today’s edge devices are typically equipped with powerful accelerators within their architecture to efficiently process the vast amount of data generated in place. In this paper, we evaluate major state-of-the-art edge devices in the context of object detection, which is one of the principal applications of modern AI technology. For our evaluation study, we choose recent devices with different accelerators to compare performance behavior depending on different architectural characteristics. The accelerators studied in this work include the GPU and the edge version of the TPU, and these accelerators can be used to boost the performance of deep learning operations. By performing a set of major object detection neural network benchmarks on the devices and by analyzing their performance behavior, we assess the effectiveness and capability of the modern edge devices accelerated by a powerful parallel hardware. Based on the benchmark results in the perspectives of detection accuracy, inference latency, and energy efficiency, we provide a latest report of comparative evaluation for major modern edge devices in the context of the object detection application of the AI technology.

Keywords

• edge computing
• object detection
• GPU (graphics processing unit)
• TPU (tensor processing unit)
• deep learning