Convergence of Edge Computing and Deep Learning: A Comprehensive Survey
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Abstract
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.
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The record
- Venue
- IEEE Communications Surveys & Tutorials
- Topic
- IoT and Edge/Fog Computing
- Field
- Computer Science
- Canadian institutions
- University of British Columbia
- Funders
- National Key Research and Development Program of ChinaMinistry of Education - SingaporeNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaEnergy Market Authority of Singapore
- Keywords
- Edge computingComputer scienceCloud computingEdge deviceEnhanced Data Rates for GSM EvolutionApplications of artificial intelligenceArtificial intelligenceData scienceService (business)Distributed computing
- Has abstract in OpenAlex
- yes