Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing
arXiv:1905.10083
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 interest. However, research on edge intelligence is still in its infancy stage, and a dedicated venue for exchanging the recent advances of edge intelligence is highly desired by both the computer system and artificial intelligence communities. To this end, we conduct a comprehensive survey of the recent research efforts on edge intelligence. Specifically, we first review the background and motivation for artificial intelligence running at the network edge. We then provide an overview of the overarching architectures, frameworks and emerging key technologies for deep learning model towards training/inference at the network edge. Finally, we discuss future research opportunities on edge intelligence. We believe that this survey will elicit escalating attentions, stimulate fruitful discussions and inspire further research ideas on edge intelligence.
Zhi Zhou, Xu Chen, En Li, Liekang Zeng, Ke Luo, and Junshan Zhang, "Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing," Proceedings of the IEEE
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Cited by in corpus (6)
- Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing
- Performance Analysis and Comparison of Machine and Deep Learning Algorithms for IoT Data Classification
- Data-Importance Aware User Scheduling for Communication-Efficient Edge Machine Learning
- AdaDeep: A Usage-Driven, Automated Deep Model Compression Framework for Enabling Ubiquitous Intelligent Mobiles
- Cross-stakeholder service orchestration for B5G through capability provisioning
- Now that I can see, I can improve: Enabling data-driven finetuning of CNNs on the edge