Edge Intelligence in the Making

TRAMA

With the explosive growth of mobile computing and Internet of Things (IoT) applications, as exemplified by AR/VR, smart city, and video/audio surveillance, billions of mobile and IoT devices are being connected to the Internet, generating zillions of bytes of data at the network edge. Driven by this trend, there is an urgent need to push the frontiers of artificial intelligence (AI) to the network edge to fully unleash the potential of IoT big data. Indeed, the marriage of edge computing and AI has resulted in innovative solutions, namely edge intelligence or edge AI. Nevertheless, research and practice on this emerging inter-disciplinary field is still in its infancy stage. To facilitate the dissemination of the recent advances in edge intelligence in both academia and industry, this book conducts a comprehensive and detailed survey of the recent research efforts and also showcases the authors' own research progress on edge intelligence. Specifically, the book first reviewsthe background and present motivation for AI running at the network edge. Next, it provides an overview of the overarching architectures, frameworks, and emerging key technologies for deep learning models toward training/inference at the network edge. To illustrate the research problems for edge intelligence, the book also showcases four of the authors' own research projects on edge intelligence, ranging from rigorous theoretical analysis to studies based on realistic implementation. Finally, it discusses the applications, marketplace, and future research opportunities of edge intelligence. This emerging interdisciplinary field offers many open problems and yet also tremendous opportunities, and this book only touches the tip of iceberg. Hopefully, this book will elicit escalating attention, stimulate fruitful discussions, and open new directions on edge intelligence.

SOMMARIO

Preface.- Acknowledgments.- Introduction to Edge Intelligence.- Edge Intelligence via Model Training.- Edge-Cloud Collaborative Learning via Distributionally Robust Optimization.- Hierarchical Mobile-Edge-Cloud Model Training with Hybrid Parallelism.- Edge Intelligence via Model Inference.- On-Demand Accelerating Deep Neural Network Inference via Edge Computing.- Applications, Marketplaces, and Future Directions of Edge Intelligence.- Bibliography.- Authors' Biographies.

AUTORE

Sen Lin received his B.Eng. degree in Electrical Engineering from Zhejiang University, Hangzhou, China, in 2013, and his M.S. degree in Telecommunications from The Hong Kong University of Science and Technology, Hong Kong, in 2014. Currently, he is pursuing a Ph.D. degree at the School of Electrical, Computer, and Energy Engineering at Arizona State University, Tempe, AZ, USA. His current research interests include statistical machine learning, reinforcement learning, and edge computing.Zhi Zhou received B.S., M.E., and Ph.D. degrees in 2012, 2014, and 2017, respectively, all from the School of Computer Science and Technology at Huazhong University of Science and Technology (HUST), Wuhan, China. He is currently a research fellow in the School of Data and Computer Science at Sun Yat-sen University, Guangzhou, China. In 2016, he was a Visiting Scholar at University of Göttingen. He was nominated for the 2019 CCF Outstanding Doctoral Dissertation Award, the sole recipient of the 2018ACM Wuhan & Hubei Computer Society Doctoral Dissertation Award, and a recipient of the Best Paper Award of IEEE UIC 2018. His research interests include edge computing, cloud computing, and distributed systems.Zhaofeng Zhang received his B.Eng. degree in Electrical Engineering from Huazhong University of Science and Technology, Wuhan, China, in 2015, and his M.S. degree in Electrical Engineering from Arizona State University, Tempe, AZ, USA, in 2017. Currently, he is pursuing a Ph.D. degree at the School of Electrical, Computer, and Energy Engineering in Arizona State University, Tempe, AZ, USA. His current research interests include edge computing, statistical machine learning, and optimization.Xu Chen received a Ph.D. degree in Information Engineering from the Chinese University of Hong Kong, in 2012. He is a Full Professor with Sun Yat-sen University, Guangzhou, China, and the Vice Director of the National and Local Joint Engineering Laboratory of Digital HomeInteractive Applications. He was a Post-Doctoral Research Associate with Arizona State University, Tempe, USA, from 2012–2014, and a Humboldt Scholar Fellow with the Institute of Computer Science at the University of Göttingen, Germany, from 2014– 2016. He was a recipient of the Prestigious Humboldt Research Fellowship awarded by the Alexander von Humboldt Foundation of Germany, the 2014 Hong Kong Young Scientist Runner-Up Award, the 2017 IEEE Communication Society Asia–Pacific Outstanding Young Researcher Award, the 2017 IEEE ComSoc Young Professional Best Paper Award, the Honorable Mention Award at the 2010 IEEE international conference on Intelligence and Security Informatics, the Best Paper Runner-Up Award at the 2014 IEEE International Conference on Computer Communications (INFOCOM), and the Best Paper Award at the 2017 IEEE International Conference on Communications. He is currently an Area Editor at the IEEE Open Journal of the Communications Society, an Associate Editor of theIEEE Transactions Wireless Communications, IEEE Internet of Things Journal, and IEEE Journal on Selected Areas in Communications ( JSAC) Series on Network Softwarization and Enablers.Junshan Zhang received his Ph.D. degree from the School of ECE at Purdue University, in 2000. He joined the School of ECEE at Arizona State University in August 2000 and has been Fulton Chair Professor there since 2015. His research interests fall in the general field of information networks and data science, including communication networks, edge computing and machine learning for IoT, mobile social networks, and smart grid. His current research focuses on fundamental problems in information networks and data science, including edge computing and machine learning in IoT and 5G, IoT data privacy/security, information theory, stochastic modeling, and control for smart grid. Prof. Zhang is a Fellow of the IEEE and a recipient of the ONR Young Investigator Award in 2005 and the NSF CAREER award in2003. He received the IEEE Wireless Communication Technical Committee Recognition Award in 2016. His papers have won a few awards, including the Best Student Paper Award at WiOPT 2018, the Kenneth C. Sevcik Outstanding Student Paper Award at ACM SIGMETRICS/IFIP Performance 2016, the Best Paper Runner-up Award at IEEE INFOCOM 2009 and IEEE INFOCOM 2014, and the Best Paper Award at IEEE ICC 2008 and ICC 2017. Building on his research findings, he co-founded Smartiply Inc, a Fog Computing startup company delivering boosted network connectivity and embedded artificial intelligence. Prof. Zhang was TPC co-chair for a number of major conferences in communication networks, including IEEE INFOCOM 2012 and ACM MOBIHOC 2015. He was the general chair for ACM/IEEE SEC 2017, WiOPT 2016, and IEEE Communication Theory Workshop 2007. He was a Distinguished Lecturer of the IEEE Communications Society. He is currently serving as Editor-in-chief for IEEE Transactions on Wireless Communications and a senior

ALTRE INFORMAZIONI

• Condizione: Nuovo
• ISBN: 9783031012525
• Collana: Synthesis Lectures on Learning, Networks, and Algorithms
• Dimensioni: 235 x 191 mm
• Formato: Brossura
• Illustration Notes: CCXV, 17 p.
• Pagine Arabe: 17
• Pagine Romane: ccxv