2019
DOI: 10.1109/jproc.2019.2918951
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Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing

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 t… Show more

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Cited by 1,389 publications
(603 citation statements)
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References 106 publications
(130 reference statements)
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“…Discussions on the topic of mobile DNN computation have recently obtained growing attention. By hosting artificial intel-ligence on mobile devices, mobile DNN computation deploys DNN models close to users in order to achieve more flexible execution as well as more secure interaction [15]. However, it is challenging to directly execute the computation-intensive DNNs on mobile devices due to the limited computation resource.…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…Discussions on the topic of mobile DNN computation have recently obtained growing attention. By hosting artificial intel-ligence on mobile devices, mobile DNN computation deploys DNN models close to users in order to achieve more flexible execution as well as more secure interaction [15]. However, it is challenging to directly execute the computation-intensive DNNs on mobile devices due to the limited computation resource.…”
Section: Related Workmentioning
confidence: 99%
“…The principal idea of edge computing [6]- [8] is to sink the cloud computing capability from the network core to the network edges (e.g., base stations and WLAN) in close proximity to end devices [9]- [14]. This novel feature enables computation-intensive and latency-critical DNN-based applications to be executed in a real-time responsive manner (i.e., edge intelligence) [15]. By leveraging edge computing, we can design an on-demand lowlatency DNNs inference framework for supporting real-time edge AI applications.…”
Section: Introductionmentioning
confidence: 99%
“…Methods have been proposed for both co-locating computing resources with sensors, and for offloading computing from mobile devices to nearby edge computers [22][23][24]. Increasingly, researchers want to run deep neural networks on edge devices [25][26][27], leading to the need to adapt computationally expensive deep networks for resource-constrained environments. Quantization, as discussed above, is one such approach [10,[28][29][30].…”
Section: Related Workmentioning
confidence: 99%
“…To deal with the problem, some researchers put forward their definitions. For example, Zhou et al believe that the scope of Edge Intelligence should not be restricted to running AI models solely on the edge servers or devices but in the manner of the collaboration of edge and cloud [4]. They define six levels of Edge Intelligence, from cloud-edge co-inference (level 1) to all on-device (level 6).…”
Section: Introductionmentioning
confidence: 99%