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Edge Computing for Artificial Intelligence

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Edge AI

Abstract

Extensive deployment of AI services, especially mobile AI, requires the support of edge computing. This support is not just at the network architecture 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 offloading of AI computation; (3) well-designed edge computing frameworks can better maintain AI services running on the edge; (4) fair platforms for evaluating Edge AI performance help further evolve the above implementations.

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Authors and Affiliations

  1. College of Intelligence and Computing, Tian** University Tian**, Tian**, China

    **aofei Wang & Yiwen Han

  2. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China

    Victor C. M. Leung

  3. School of Computer and Engineering, Nanyang Technological University, Singapore, Singapore

    Dusit Niyato

  4. 2012 Lab, Huawei Technologies (China), Shenzhen, China

    Xueqiang Yan

  5. School of Data and Computer Science, Sun Yat-sen University, Guangzhou, Guangdong, China

    Xu Chen

Authors
  1. **aofei Wang
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  4. Dusit Niyato
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Wang, X., Han, Y., Leung, V.C.M., Niyato, D., Yan, X., Chen, X. (2020). Edge Computing for Artificial Intelligence. In: Edge AI. Springer, Singapore. https://doi.org/10.1007/978-981-15-6186-3_7

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  • DOI: https://doi.org/10.1007/978-981-15-6186-3_7

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  • Print ISBN: 978-981-15-6185-6

  • Online ISBN: 978-981-15-6186-3

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