@inproceedings{e407d22aa2d94f44a1d42cfbab7df253,
title = "Deep Learning for Edge Computing: Current Trends, Cross-Layer Optimizations, and Open Research Challenges",
abstract = "In the Machine Learning era, Deep Neural Networks (DNNs) have taken the spotlight, due to their unmatchable performance in several applications, such as image processing, computer vision, and natural language processing. However, as DNNs grow in their complexity, their associated energy consumption becomes a challenging problem. Such challenge heightens for edge computing, where the computing devices are resource-constrained while operating on limited energy budget. Therefore, specialized optimizations for deep learning have to be performed at both software and hardware levels. In this paper, we comprehensively survey the current trends of such optimizations and discuss key open research mid-term and long-term challenges.",
keywords = "accelerator, deep learning, DNN, edge computing, energy efficiency, hardware, IoT, low power, neural networks, performance, pre-processing, pruning, quantization, software",
author = "Alberto Marchisio and Hanif, {Muhammad Abdullah} and Faiq Khalid and George Plastiras and Christos Kyrkou and Theocharis Theocharides and Muhammad Shafique",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.; 18th IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2019 ; Conference date: 15-07-2019 Through 17-07-2019",
year = "2019",
month = jul,
doi = "10.1109/ISVLSI.2019.00105",
language = "English (US)",
series = "Proceedings of IEEE Computer Society Annual Symposium on VLSI, ISVLSI",
publisher = "IEEE Computer Society Press",
pages = "553--559",
booktitle = "Proceedings - 2019 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2019",
}