Deep Learning for the Internet of Things
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, ef...
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| Published in | Computer (Long Beach, Calif.) Vol. 51; no. 5; pp. 32 - 41 |
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| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
IEEE
01.05.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| Online Access | Get full text |
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| 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. |
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| AbstractList | 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. |
| Author | Yao, Shuochao Hu, Shaohan Zhang, Chao Su, Lu Zhang, Aston Zhao, Yiran Shao, Huajie Abdelzaher, Tarek |
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| SubjectTerms | Computational modeling Deep learning embedded learning Embedded systems Feature extraction Internet of Things IoT Machine learning mobile and embedded deep learning Neural networks Task analysis |
| Title | Deep Learning for the Internet of Things |
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