Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing
Zhi Zhou, Xu Chen, En Li, Liekang Zeng, Ke Luo, Junshan Zhang

TL;DR
This paper surveys recent advances in edge intelligence, highlighting how AI is being pushed to the network edge through edge computing to enable real-time, privacy-preserving applications on mobile and IoT devices.
Contribution
It provides a comprehensive overview of architectures, frameworks, and key technologies for deploying deep learning at the network edge, and discusses future research directions.
Findings
Edge computing enables AI tasks at the network edge.
Deep learning frameworks are adapting for edge deployment.
Future opportunities include privacy, efficiency, and scalability challenges.
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 inter-discipline, edge AI or edge intelligence, is beginning to receive a tremendous amount of…
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Taxonomy
TopicsIoT and Edge/Fog Computing · Advanced Neural Network Applications · Context-Aware Activity Recognition Systems
