@misc{94ca4272fbb244418572d2d29c6ef6ae,
title = "Deep Learning for the Internet of Things",
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.",
keywords = "Internet of Things, IoT, deep learning, embedded learning, machine learning, mobile and embedded deep learning, neural networks",
author = "Shuochao Yao and Yiran Zhao and Aston Zhang and Shaohan Hu and Huajie Shao and Chao Zhang and Lu Su and Tarek Abdelzaher",
note = "Funding Information: Research reported in this paper was sponsored in part by NSF under grants CNS 16-18627 and CNS 13-20209 and in part by the Army Research Laboratory under Cooperative Agreements W911NF- 09-2-0053 and W911NF-17-2-0196. The views and conclusions contained in this document are those of the authors and should not be interpreted as represent ing the official policies, either expressed or implied, of the Army Research Laboratory, NSF, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on. Publisher Copyright: {\textcopyright} 1970-2012 IEEE.",
year = "2018",
month = may,
doi = "10.1109/MC.2018.2381131",
language = "English (US)",
volume = "51",
pages = "32--41",
journal = "Computer",
issn = "0018-9162",
publisher = "IEEE Computer Society",
}