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Convergence of Edge Computing and Deep Learning: A Comprehensive Survey
Ubiquitous sensors and smart devices from factories and communities are
generating massive amounts of data, and ever-increasing computing power is
driving the core of computation and services from the cloud to the edge of the
network. As an important enabler broadly changing people's lives, from face
recognition to ambitious smart factories and cities, developments of artificial
intelligence (especially deep learning, DL) based applications and services are
thriving. However, due to efficiency and latency issues, the current cloud
computing service architecture hinders the vision of "providing artificial
intelligence for every person and every organization at everywhere". Thus,
unleashing DL services using resources at the network edge near the data
sources has emerged as a desirable solution. Therefore, edge intelligence,
aiming to facilitate the deployment of DL services by edge computing, has
received significant attention. In addition, DL, as the representative
technique of artificial intelligence, can be integrated into edge computing
frameworks to build intelligent edge for dynamic, adaptive edge maintenance and
management. With regard to mutually beneficial edge intelligence and
intelligent edge, this paper introduces and discusses: 1) the application
scenarios of both; 2) the practical implementation methods and enabling
technologies, namely DL training and inference in the customized edge computing
framework; 3) challenges and future trends of more pervasive and fine-grained
intelligence. We believe that by consolidating information scattered across the
communication, networking, and DL areas, this survey can help readers to
understand the connections between enabling technologies while promoting
further discussions on the fusion of edge intelligence and intelligent edge,
i.e., Edge DL.Comment: To be published in IEEE Communications Surveys and Tutorial