Yuanming Shi, Kai Yang, Zhanpeng Yang

Mobile Edge Artificial Intelligence

Opportunities and Challenges

• Broschiertes Buch

Produktbeschreibung

Mobile Edge Artificial Intelligence: Opportunities and Challenges presents recent advances in wireless technologies and nonconvex optimization techniques for designing efficient edge AI systems. The book includes comprehensive coverage on modeling, algorithm design and theoretical analysis. Through typical examples, the powerfulness of this set of systems and algorithms is demonstrated, along with their abilities to make low-latency, reliable and private intelligent decisions at network edge. With the availability of massive datasets, high performance computing platforms, sophisticated algorithms and software toolkits, AI has achieved remarkable success in many application domains.

As such, intelligent wireless networks will be designed to leverage advanced wireless communications and mobile computing technologies to support AI-enabled applications at various edge mobile devices with limited communication, computation, hardware and energy resources.

Produktdetails

• Verlag: Academic Press / Elsevier Science & Technology
• Artikelnr. des Verlages: C2020-0-00624-9
• Englisch
• Abmessung: 9mm x 152mm x 229mm
• Gewicht: 340g
• ISBN-13: 9780128238172
• Artikelnr.: 67578722

Autorenporträt

Yuanming Shi received the B.S. degree in electronic engineering from Tsinghua University, Beijing, China, in 2011. He received the Ph.D. degree in electronic and computer engineering from The Hong Kong University of Science and Technology (HKUST), in 2015. Since September 2015, he has been with the School of Information Science and Technology in ShanghaiTech University, where he is currently a tenured Associate Professor. He visited University of California, Berkeley, CA, USA, from October 2016 to February 2017. His research areas include optimization, machine learning, wireless communications, and their applications to 6G, IoT, and edge AI. He was a recipient of the 2016 IEEE Marconi Prize Paper Award in Wireless Communications, the 2016 Young Author Best Paper Award by the IEEE Signal Processing Society, and the 2021 IEEE ComSoc Asia-Pacific Outstanding Young Researcher Award. He is also an editor of IEEE Transactions on Wireless Communications, IEEE Journal on Selected Areas in

Communications, and Journal of Communications and Information Networks.

Mr. Kai Yang is currently at JD.com, Inc., China. His research interests include big data processing, mobile edge/fog computing, mobile edge artificial intelligence and dense communication networking. He has developed a wireless distributed computing framework for edge inference, and an over-the-air computation approach for edge federated machine learning.

Inhaltsangabe

I. Introduction and Overview 1. Primer on Artificial Intelligence 2. Overview of Edge AI Systems

II. Edge Inference 3. Model Compression for On-Device Inference 4. Wireless MapReduce for Device Distributed Inference 5. Wireless Cooperative Transmission for Edge Inference

III. Edge Training 6. Over-the-Air Computation for Federated Learning 7. Blind Over-the-Air Computation for Federated Learning 8. Reconfigurable Intelligent Surface Aided Federated Learning System

IV. Future Directions 9. Communication-Efficient Algorithms for Edge AI 10. Future Research Directions