Time-Sensitive Networking to meet Hard-real Time Boundaries on Edge Intelligence Applications

This paper introduces an AI video analytic application that has been implemented on an edge-computing device. The application is designed to perform real-time object detection, specifically targeting road signaling cones. The primary focus of this work is to demonstrate the device's capability to accelerate the inference of AI models and video compression using dedicated hardware. Moreover, the critical information, including the location and size of the objects detected, is transmitted as hard-real-time traffic using deterministic Ethernet. This use case has been implemented in the context of the NATO Generic Architecture for Land Systems (NGVA). The paper provides an overview of the approach taken, including the hardware and software used, as well as the design flow followed to implement the solution. The results of the implementation are discussed in the concluding section of the paper, along with areas for future work.