Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing

With the breakthroughs in deep learning, the recent years have witnessed a booming of artificial intelligence (AI) applications and services, spanning from personal assistant to recommendation systems to video/audio surveillance. More recently, with the proliferation of mobile computing and Internet...

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Vydáno v:	Proceedings of the IEEE Ročník 107; číslo 8; s. 1738 - 1762
Hlavní autoři:	Zhou, Zhi, Chen, Xu, Li, En, Zeng, Liekang, Luo, Ke, Zhang, Junshan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.08.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Alliances Artificial intelligence Computational modeling Computer architecture Deep learning Edge computing edge intelligence Electronic devices Internet of Things Machine learning Mobile computing Motivation Recommender systems Task analysis Training
ISSN:	0018-9219, 1558-2256
On-line přístup:	Získat plný text
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Abstract	With the breakthroughs in deep learning, the recent years have witnessed a booming of artificial intelligence (AI) applications and services, spanning from personal assistant to recommendation systems to video/audio surveillance. More recently, with the proliferation of mobile computing and Internet of Things (IoT), billions of mobile and IoT devices are connected to the Internet, generating zillions bytes of data at the network edge. Driving by this trend, there is an urgent need to push the AI frontiers to the network edge so as to fully unleash the potential of the edge big data. To meet this demand, edge computing, an emerging paradigm that pushes computing tasks and services from the network core to the network edge, has been widely recognized as a promising solution. The resulted new interdiscipline, edge AI or edge intelligence (EI), is beginning to receive a tremendous amount of interest. However, research on EI is still in its infancy stage, and a dedicated venue for exchanging the recent advances of EI is highly desired by both the computer system and AI communities. To this end, we conduct a comprehensive survey of the recent research efforts on EI. Specifically, we first review the background and motivation for AI running at the network edge. We then provide an overview of the overarching architectures, frameworks, and emerging key technologies for deep learning model toward training/inference at the network edge. Finally, we discuss future research opportunities on EI. We believe that this survey will elicit escalating attentions, stimulate fruitful discussions, and inspire further research ideas on EI.
AbstractList	With the breakthroughs in deep learning, the recent years have witnessed a booming of artificial intelligence (AI) applications and services, spanning from personal assistant to recommendation systems to video/audio surveillance. More recently, with the proliferation of mobile computing and Internet of Things (IoT), billions of mobile and IoT devices are connected to the Internet, generating zillions bytes of data at the network edge. Driving by this trend, there is an urgent need to push the AI frontiers to the network edge so as to fully unleash the potential of the edge big data. To meet this demand, edge computing, an emerging paradigm that pushes computing tasks and services from the network core to the network edge, has been widely recognized as a promising solution. The resulted new interdiscipline, edge AI or edge intelligence (EI), is beginning to receive a tremendous amount of interest. However, research on EI is still in its infancy stage, and a dedicated venue for exchanging the recent advances of EI is highly desired by both the computer system and AI communities. To this end, we conduct a comprehensive survey of the recent research efforts on EI. Specifically, we first review the background and motivation for AI running at the network edge. We then provide an overview of the overarching architectures, frameworks, and emerging key technologies for deep learning model toward training/inference at the network edge. Finally, we discuss future research opportunities on EI. We believe that this survey will elicit escalating attentions, stimulate fruitful discussions, and inspire further research ideas on EI.
Author	Zhang, Junshan Zhou, Zhi Luo, Ke Chen, Xu Li, En Zeng, Liekang
Author_xml	– sequence: 1 givenname: Zhi orcidid: 0000-0002-0987-9344 surname: Zhou fullname: Zhou, Zhi email: zhouzhi9@mail.sysu.edu.cn organization: School of Data and Computer Science, Sun Yat-sen University (SYSU), Guangzhou, China – sequence: 2 givenname: Xu orcidid: 0000-0001-9943-6020 surname: Chen fullname: Chen, Xu email: chenxu35@mail.sysu.edu.cn organization: School of Data and Computer Science, Sun Yat-sen University (SYSU), Guangzhou, China – sequence: 3 givenname: En orcidid: 0000-0002-9928-5813 surname: Li fullname: Li, En email: lien5@mail2.sysu.edu organization: School of Data and Computer Science, Sun Yat-sen University (SYSU), Guangzhou, China – sequence: 4 givenname: Liekang orcidid: 0000-0003-4800-8768 surname: Zeng fullname: Zeng, Liekang email: zenglk3@mail2.sysu.edu organization: School of Data and Computer Science, Sun Yat-sen University (SYSU), Guangzhou, China – sequence: 5 givenname: Ke orcidid: 0000-0003-0118-7236 surname: Luo fullname: Luo, Ke email: luok7@mail2.sysu.edu organization: School of Data and Computer Science, Sun Yat-sen University (SYSU), Guangzhou, China – sequence: 6 givenname: Junshan orcidid: 0000-0002-3840-1753 surname: Zhang fullname: Zhang, Junshan email: junshan.zhang@asu.edu organization: School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA
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Snippet	With the breakthroughs in deep learning, the recent years have witnessed a booming of artificial intelligence (AI) applications and services, spanning from...
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SubjectTerms	Alliances Artificial intelligence Computational modeling Computer architecture Deep learning Edge computing edge intelligence Electronic devices Internet of Things Machine learning Mobile computing Motivation Recommender systems Task analysis Training
Title	Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing
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