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Authors
Advisor(s)
Abstract(s)
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.
Description
Este trabalho foi financiado pelo Concurso Anual para Projetos de Investigação, Desenvolvimento, Inovação e Criação Artística (IDI&CA) 2019 do Instituto Politécnico de Lisboa. Código de referência IPL/2019/inCNeuraINet_ISEL
Keywords
Artificial intelligence Deep learning Deep neural network Edge computing
Citation
VÉSTIAS, Mário P.; [et al] – Moving deep learning to the edge. Algorithms. ISSN 1999-4893. Vol. 13, N.º 5 (2020), pp. 1-33
Publisher
MDPI