Deep Learning for the Internet of Things

Shuochao Yao; Yiran Zhao; Aston Zhang; Shaohan Hu; Huajie Shao; Chao Zhang; Lü Su; Tarek Abdelzaher

doi:10.1109/mc.2018.2381131

Deep Learning for the Internet of Things

Shuochao Yao (University of Illinois Urbana-Champaign) iD Yiran Zhao (University of Illinois Urbana-Champaign) iD Aston Zhang (Amazon (United States)) Shaohan Hu (IBM Research - Thomas J. Watson Research Center) iD Huajie Shao (University of Illinois Urbana-Champaign) iD Chao Zhang (University of Illinois Urbana-Champaign) iD Lü Su (University at Buffalo, State University of New York) iD Tarek Abdelzaher (University of Illinois Urbana-Champaign) iD

Journal: Computer • 2018-05-01 • IEEE Computer Society • ISSN: 0018-9162

Abstract

How can the advantages of deep learning be brought to the emerging world of embedded IoT devices? The authors discuss several core challenges in embedded and mobile deep learning, as well as recent solutions demonstrating the feasibility of building IoT applications that are powered by effective, efficient, and reliable deep learning models.

Subjects & Keywords

# Advanced Neural Network Applications # Computer Science # IoT and Edge/Fog Computing # Internet of Things and AI

How to Cite this Article

                    Shuochao Yao, Yiran Zhao, Aston Zhang, et al. (2018). "Deep Learning for the Internet of Things". Computer. https://doi.org/10.1109/mc.2018.2381131                

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Identifiers & Provenance

DOI: https://doi.org/10.1109/mc.2018.2381131 ↗
OpenAlex: W2804686008 ↗
Primary Index: OPENALEX