Abstract
Artificial intelligence and edge computing are expected to benefit each other. This book has comprehensively introduced and discussed various applicable scenarios and fundamental enabling techniques for edge intelligence and intelligent edge. 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 architecture to achieve the best performance of AI training and inference.
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Artificial intelligence and edge computing are expected to benefit each other. This book has comprehensively introduced and discussed various applicable scenarios and fundamental enabling techniques for edge intelligence and intelligent edge. 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 architecture to achieve the best performance of AI training and inference. As the computing power of the edge increases, edge intelligence will become common, and intelligent edge will play an important supporting role to improve the performance of edge intelligence. We hope that this survey will increase discussions and research efforts on AI/edge integration that will advance future edge AI applications and services.
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© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Wang, X., Han, Y., Leung, V.C.M., Niyato, D., Yan, X., Chen, X. (2020). Conclusions. In: Edge AI. Springer, Singapore. https://doi.org/10.1007/978-981-15-6186-3_10
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DOI: https://doi.org/10.1007/978-981-15-6186-3_10
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Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-6185-6
Online ISBN: 978-981-15-6186-3
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