The Deep (Learning) Transformation of Mobile and Embedded Computing

Mobile and embedded devices increasingly rely on deep neural networks to understand the world-a feat that would have overwhelmed their system resources only a few years ago. Further integration of machine learning and embedded/mobile systems will require additional breakthroughs of efficient learnin...

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Bibliographic Details
Published inComputer (Long Beach, Calif.) Vol. 51; no. 5; pp. 12 - 16
Main Authors Lane, Nicholas D., Warden, Pete
Format Journal Article
LanguageEnglish
Published New York IEEE 01.05.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Artificial neural networks
Computer architecture
Deep learning
deep model compression
Electronic devices
embedded deep learning
Embedded systems
intelligent systems
Machine learning
machine learning systems
mobile
Mobile communication systems
Mobile computing
mobile deep neural networks
Neural networks
pervasive computing
Variation
Online AccessGet full text
ISSN0018-9162
1558-0814
DOI10.1109/MC.2018.2381129

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Summary:Mobile and embedded devices increasingly rely on deep neural networks to understand the world-a feat that would have overwhelmed their system resources only a few years ago. Further integration of machine learning and embedded/mobile systems will require additional breakthroughs of efficient learning algorithms that can function under fluctuating resource constraints, giving rise to a field that straddles computer architecture, software systems, and artificial intelligence.
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ISSN:0018-9162
1558-0814
DOI:10.1109/MC.2018.2381129