@inproceedings{049206706aee4000bec7aec80c86e040,
title = "Latency control for distributed machine vision at the edge through approximate computing",
abstract = "Multicamera based Deep Learning vision applications subscribe to the Edge computing paradigm due to stringent latency requirements. However, guaranteeing latency in the wireless communication links between the cameras nodes and the Edge server is challenging, especially in the cheap and easily available unlicensed bands due to the interference from other camera nodes in the system, and from external sources. In this paper, we show how approximate computation techniques can be used to design a latency controller that uses multiple video frame image quality control knobs to simultaneously satisfy latency and accuracy requirements for machine vision applications involving object detection, and human pose estimation. Our experimental results on an Edge test bed indicate that the controller is able to correct for up to 164% degradation in latency due to interference within a settling time of under 1.15 s.",
keywords = "Approximate computing, Edge computing, Latency control, Machine vision",
author = "Anjus George and Arun Ravindran",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 3rd International Conference on Edge Computing, EDGE 2019 held as Part of the Services Conference Federation, SCF 2019 ; Conference date: 25-06-2019 Through 30-06-2019",
year = "2019",
doi = "10.1007/978-3-030-23374-7_2",
language = "English",
isbn = "9783030233730",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "16--30",
editor = "Tao Zhang and Jinpeng Wei and Liang-Jie Zhang",
booktitle = "Edge Computing – EDGE 2019 - 3rd International Conference, Held as Part of the Services Conference Federation, SCF 2019, Proceedings",
}