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Moving Deep Learning to the Edge

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Instituto de Engenharia de Sistemas e Computadores-Investigação e Desenvolvimento (INESC-ID), Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, 1959-007 Lisbon, Portugal
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Instituto de Engenharia de Sistemas e Computadores-Investigação e Desenvolvimento (INESC-ID), Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal
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Author to whom correspondence should be addressed.
Algorithms 2020, 13(5), 125; https://doi.org/10.3390/a13050125
Received: 29 March 2020 / Revised: 16 May 2020 / Accepted: 16 May 2020 / Published: 18 May 2020
(This article belongs to the Section Evolutionary Algorithms and Machine Learning)
Deep learning is now present in a wide range of services and applications, replacing and complementing other machine learning algorithms. Performing training and inference of deep neural networks using the cloud computing model is not viable for applications where low latency is required. Furthermore, the rapid proliferation of the Internet of Things will generate a large volume of data to be processed, which will soon overload the capacity of cloud servers. One solution is to process the data at the edge devices themselves, in order to alleviate cloud server workloads and improve latency. However, edge devices are less powerful than cloud servers, and many are subject to energy constraints. Hence, new resource and energy-oriented deep learning models are required, as well as new computing platforms. This paper reviews the main research directions for edge computing deep learning algorithms. View Full-Text
Keywords: artificial intelligence; deep learning; deep neural network; edge computing artificial intelligence; deep learning; deep neural network; edge computing
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MDPI and ACS Style

Véstias, M.P.; Duarte, R.P.; de Sousa, J.T.; Neto, H.C. Moving Deep Learning to the Edge. Algorithms 2020, 13, 125.

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