Abstract
With the advent of the Internet of Everything, the proliferation of data has put a huge burden on data centers and network bandwidth. To ease the pressure on data centers, edge computing, a new computing paradigm, is gradually gaining attention. Meanwhile, artificial intelligence services based on deep learning are also thriving. However, such intelligent services are usually deployed in data centers, which cause high latency. The combination of edge computing and artificial intelligence provides an effective solution to this problem. This new intelligence paradigm is called edge intelligence. In this paper, we focus on edge training and edge inference, the prior training models using local data at the resource-constrained edge devices. The latter deploying models at the edge devices through model compression and inference acceleration. This paper provides a comprehensive survey of existing architectures, technologies, frameworks and implementations in these two areas, and discusses existing challenges, possible solutions and future directions. We believe that this survey will make more researchers aware of edge intelligence.
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Acknowledgements
The authors are very appreciative to the reviewers for their precious comments which enormously ameliorated the quality of this paper. This work was supported in part by National Key R&D Program of China (2018YFB1701802); National Natural Science Foundation of China 61802280,61806143, 61772365, 41772123; Tianjin Technology Innovation Guide Special 21YDTPJC00130.
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Su, W., Li, L., Liu, F. et al. AI on the edge: a comprehensive review. Artif Intell Rev 55, 6125–6183 (2022). https://doi.org/10.1007/s10462-022-10141-4
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DOI: https://doi.org/10.1007/s10462-022-10141-4