OpenEI: An Open Framework for Edge Intelligence
Xingzhou Zhang,Yifan Wang,Sidi Lu,Liangkai Liu,Lanyu Xu,Weisong Shi +5 more
- 07 Jul 2019
- pp 1840-1851
TL;DR: An Open Framework for Edge Intelligence (OpenEI), which is a lightweight software platform to equip edges with intelligent processing and data sharing capability and analyzes four fundamental EI techniques used to build OpenEI and identifies several open problems based on potential research directions.
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Abstract: In the last five years, edge computing has attracted tremendous attention from industry and academia due to its promise to reduce latency, save bandwidth, improve availability, and protect data privacy to keep data secure. At the same time, we have witnessed the proliferation of AI algorithms and models which accelerate the successful deployment of intelligence mainly in cloud services. These two trends, combined together, have created a new horizon: Edge Intelligence (EI). The development of EI requires much attention from both the computer systems research community and the AI community to meet these demands. However, existing computing techniques used in the cloud are not applicable to edge computing directly due to the diversity of computing sources and the distribution of data sources. We envision that there missing a framework that can be rapidly deployed on edge and enable edge AI capabilities. To address this challenge, in this paper we first present the definition and a systematic review of EI. Then, we introduce an Open Framework for Edge Intelligence (OpenEI), which is a lightweight software platform to equip edges with intelligent processing and data sharing capability. We analyze four fundamental EI techniques which are used to build OpenEI and identify several open problems based on potential research directions. Finally, four typical application scenarios enabled by OpenEI are presented.
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Convergence of Edge Computing and Deep Learning: A Comprehensive Survey
TL;DR: By consolidating information scattered across the communication, networking, and DL areas, this survey can help readers to understand the connections between enabling technologies while promoting further discussions on the fusion of edge intelligence and intelligent edge, i.e., Edge DL.
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Edge Computing for Autonomous Driving: Opportunities and Challenges
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TL;DR: In this paper, the authors review state-of-the-art approaches in these areas as well as explore potential solutions to address these challenges, including providing enough computing power, redundancy, and security so as to guarantee the safety of autonomous vehicles.
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TL;DR: In this paper, the authors divide edge intelligence into AI for edge (intelligence-enabled edge computing) and AI on edge (artificial intelligence on edge), and provide insights into this new interdisciplinary field from a broader perspective.
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