IEEE Access Special Section Editorial: Edge Computing and Networking for Ubiquitous AI

Victor C. M. Leung, Xiaofei Wang, Abbas Jamalipour, Xu Chen, Samia Bouzefrane
2021 IEEE Access  
With its rapid development recently, edge computing with processing, storage, and networking capabilities has become an important solution to break through the bottleneck of emerging technology development by virtue of its advantages in reducing data transmission, decreasing service latency, and easing cloud computing pressure. Among several application scenarios such as network optimization, intelligent manufacturing, and real-time video analytics, edge computing can work with artificial
more » ... igence (AI) synergistically. Therefore, many researchers are investigating edge computing with AI from two perspectives. One is that the emergence of AI solves the optimization problem of edge computing. For example, when network devices need to process some complex and fuzzy information, the powerful learning and reasoning ability of AI can help to extract valuable information from the massive data and realize intelligent management. Another is how edge computing supports AI in a networking environment. For example, AI training and inference can be efficiently enabled by a multitude of computing resources from edge computing. Therefore, edge computing and AI are mutually beneficial in networking. This IEEE ACCESS Special Section on Edge Computing and Networking for Ubiquitous AI aims to explore the challenges of ubiquitous intelligence potentially enabled by both edge computing and machine learning, such as what can AI do for edge computing, what edge computing can do for AI, and novel applications of edge computing with AI. The Call for Papers drew wide attention from the research community and received 46 submissions. Out of these, 14 articles were accepted for inclusion in the Special Section after a thorough review process involving at least two independent expert referees. The 14 accepted articles can be broadly categorized into three groups. The first, with five articles, tackles what AI can do for edge computing. The second group of four articles address the problems of what edge computing can do for AI. The remaining five articles discuss novel edge computing/AI applications. In the first group, the article "IKW: Inter-kernel weights for power efficient edge computing," by Udupa et al., proposes the Inter-Kernel Weight (IKW) technique, which can be used to eliminate redundant multiplications for a subset of kernel weights in a convolutional neural network (CNN) layer. The proposed IKW architecture, as an alternative
doi:10.1109/access.2021.3090143 fatcat:mdgmfeph6zcrzc6z7w7xjg3iua