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Deep Learning on Edge: Challenges and Trends

Deep Learning on Edge: Challenges and Trends

Mário P. Véstias
Copyright: © 2020 |Pages: 20
ISBN13: 9781799821120|ISBN10: 1799821129|EISBN13: 9781799821144
DOI: 10.4018/978-1-7998-2112-0.ch002
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MLA

Véstias, Mário P. "Deep Learning on Edge: Challenges and Trends." Smart Systems Design, Applications, and Challenges, edited by João M.F. Rodrigues, et al., IGI Global, 2020, pp. 23-42. https://doi.org/10.4018/978-1-7998-2112-0.ch002

APA

Véstias, M. P. (2020). Deep Learning on Edge: Challenges and Trends. In J. Rodrigues, P. Cardoso, J. Monteiro, & C. Ramos (Eds.), Smart Systems Design, Applications, and Challenges (pp. 23-42). IGI Global. https://doi.org/10.4018/978-1-7998-2112-0.ch002

Chicago

Véstias, Mário P. "Deep Learning on Edge: Challenges and Trends." In Smart Systems Design, Applications, and Challenges, edited by João M.F. Rodrigues, et al., 23-42. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-2112-0.ch002

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Abstract

Deep learning on edge has been attracting the attention of researchers and companies looking to provide solutions for the deployment of machine learning computing at the edge. A clear understanding of the design challenges and the application requirements are fundamental to understand the requirements of the next generation of edge devices to run machine learning inference. This chapter reviews several aspects of deep learning: applications, deep learning models, and computing platforms. The way deep learning is being applied to edge devices is described. A perspective of the models and computing devices being used for deep learning on edge are given, as well as what challenges face the hardware designers to guarantee the vast set of tight constraints like performance, power consumption, flexibility, etc. of edge computing platforms. Finally, a trends overview of deep learning models and architectures is discussed.

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