Characterization of Real-Time Object Detection Workloads on Vehicular Edge

As recent literature suggests the need for communication between autonomous vehicles, edge devices have emerged as a viable conduit to facilitate real-time data sharing. Edge devices strike a suitable medium between the alternatives of cloud centralization and full vehicle-to-vehicle decentralization, providing the computational savings of sending and receiving information from one place while also boosting speed by bypassing internet protocols. Given the novelty of both object detection models and autonomous vehicle-oriented edge device implementation, there are no standards for hardware and software specifications on the edge. In this project, we seek to address this void, investigating the GPU and CPU usage patterns of various object detection models and machine learning frameworks. We also aim to uncover optimization opportunities such as workload pipelining. One early difficulty was that only a few models tested achieved real-time (<33 ms) object detection. Our results show that the GPU utilization varies widely between models. One interesting is that only one CPU core is used during the inference process, suggesting the number of CPU cores will not be a bottleneck. Meanwhile, we find that increasing CPU cores proportional to the amount of traffic will likely be necessary to preserve real-time object detection.