Investigating Mobile Edge-Cloud Trade-Offs of Object Detection with YOLO

• W. F. Magalhães Universidade Federal de Campina Grande
• H. M. Gomes Universidade Federal de Campina Grande
• L. B. Marinho Universidade Federal de Campina Grande
• G. S. Aguiar Universidade Federal de Campina Grande
• P. Silveira Hewlett Packard Enterprise

DOI: https://doi.org/10.5753/kdmile.2019.8788

Resumo

With the advent of smart IoT applications empowered with AI, together with the democratization of mobile devices, moving the computation from cloud to edge is a natural trend in both academia and industry. A major challenge in this direction is enabling the deployment of Deep Neural Networks (DNNs), which usually demand lots of computational resources (i.e. memory, disk, CPU/GPU, and power), in resource limited edge devices. Among the possible strategies to tackle this challenge are: (i) running the entire DNN on the edge device (sometimes not feasible), (ii) distributing the computation between edge and cloud or (iii) running the entire DNN on the cloud. All these strategies involve trade-offs in terms of latency, communication, and financial costs. In this article we investigate such trade-offs in a real-world scenario involving object detection from video surveillance feeds. We conduct several experiments on two different versions of YOLO (You Only Look Once), a state-of-the-art DNN designed for fast and accurate object detection and location. Our experimental setup for DNN model partitioning includes a Raspberry PI 3 B+ and a cloud server equipped with a GPU. Experiments using different network bandwidths are performed. Our results provide useful insights about the aforementioned trade-offs.

Palavras-chave: deep neural networks, edge-cloud partitioning, object detection

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