Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing
👨‍🔬 Zhi Zhou, Xu Chen, En Li, Liekang Zeng, Ke Luo, Junshan Zhang
📅 2019-06-12
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🔗 DOI: https://doi.org/10.1109/jproc.2019.2918951
📊 Citations: 2061
🔓 Open Access

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.

References:
• Edge Intelligence for the Industrial Internet of Things
• Boomerang: On-Demand Cooperative Deep Neural Network Inference for Edge Intelligence on the Industrial Internet of Things
• Machine Learning at the Wireless Edge: Distributed Stochastic Gradient Descent Over-the-Air
• Data-driven Task Allocation for Multi-task Transfer Learning on the Edge
• pCAMP: Performance Comparison of Machine Learning Packages on the Edges
• Dynamic Adaptive DNN Surgery for Inference Acceleration on the Edge

#AI #IoT #Networking