In-Network Computing for Object Recognition in XR Applications

Extended Reality (XR) applications are emerging as a transformative technology, significantly enhancing user experiences in various domains, such as marketing and in-store shopping. In this context, object detection is a crucial component that enables XR-based gadgets to identify and interact with products in real-time. Object detection tasks typically employ AI-based models like YOLO (a.k.a. You Only Look Once), which require substantial computing power and currently are often offloaded to cloud servers. However, with advancements in hardware and Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) technologies, In-Network Computing (INC) has emerged as a viable alternative. This paper proposes the application of INC for XR applications, specifically for in-store shopping use case, by leveraging the capabilities of programmable network devices to distribute computational tasks across the network. By integrating and enabling INC, we aim to enhance the efficiency and scalability of the evaluated XR applications while reducing latency, bandwidth consumption and reliance on cloud servers.