Titlebook: Edge AI; Convergence of Edge Xiaofei Wang,Yiwen Han,Xu Chen Book 2020 The Editor(s) (if applicable) and The Author(s), under exclusive lic

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Artificial Intelligence Training at Edgee architecture, which consists of a large number of edge nodes with modest computing resources, can cater for alleviating the pressure of networks and protecting data privacy by processing the data or training at themselves. Training at the edge or potentially among “end–edge–cloud,” treating the ed

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Edge Computing for Artificial Intelligencee level, the design, adaptation, and optimization of edge hardware and software are equally important. Specifically, (1) customized edge hardware and corresponding optimized software frameworks and libraries can help AI execution more efficiently; (2) the edge computing architecture can enable the o

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Artificial Intelligence for Optimizing Edgent. Computation and storage capabilities of edge nodes, along with the collaboration of the cloud, make it possible to use DL to optimize edge computing networks and systems. With regard to various edge management issues such as edge caching, offloading, communication, security protection, etc., (1)

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Conclusionsle scenarios and fundamental enabling techniques for . and .. In summary, the key issue of extending AI from the cloud to the edge of the network is: under the multiple constraints of networking, communication, computing power, and energy consumption, how to devise and develop edge computing archite

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I; and e)using AI to optimize edge. After identifying these five aspects, which are essential for the fusion of edge computing and AI, it discusses current challenges and outlines future trends in achieving mor978-981-15-6188-7978-981-15-6186-3

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