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

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

Referências

Abadi, M. et al. TensorFlow: Large-scale machine learning on heterogeneous systems, 2015. Software available from tensorflow.org.

Chollet, F. et al. Keras. https://keras.io, 2015.

Hadidi, R., Cao, J., Ryoo, M. S., and Kim, H. Collaborative execution of deep neural networks on internet of
things devices. CoRR vol. abs/1901.02537, 2019.

Kang, Y., Hauswald, J., Gao, C., Rovinski, A., Mudge, T., Mars, J., and Tang, L. Neurosurgeon: Collaborative
intelligence between the cloud and mobile edge. In Proceedings of the Twenty-Second International Conference on Architectural Support for Programming Languages and Operating Systems. ACM, Xi’an, China, pp. 615–629, 2017.

Redmon, J. and Farhadi, A. YOLO9000: better, faster, stronger. CoRR vol. abs/1612.08242, 2016.

Shi, W., Hou, Y., Zhou, S., Niu, Z., Zhang, Y., and Geng, L. Improving device-edge cooperative inference of
deep learning via 2-step pruning. CoRR vol. abs/1903.03472, 2019.

Teerapittayanon, S., McDanel, B., and Kung, H. T. Distributed deep neural networks over the cloud, the edge
and end devices. In 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS). Institute of Electrical and Electronics Engineers (IEEE), Atlanta, USA, pp. 328–339, 2017.
Publicado
18/11/2019
Como Citar

Selecione um Formato
MAGALHÃES, W. F.; GOMES, H. M.; MARINHO, L. B.; AGUIAR, G. S.; SILVEIRA, P.. Investigating Mobile Edge-Cloud Trade-Offs of Object Detection with YOLO. In: SYMPOSIUM ON KNOWLEDGE DISCOVERY, MINING AND LEARNING (KDMILE) , 2019, Fortaleza. Anais do VII Symposium on Knowledge Discovery, Mining and Learning. Porto Alegre: Sociedade Brasileira de Computação, nov. 2019 . p. 49-56. DOI: https://doi.org/10.5753/kdmile.2019.8788.