Keynote Speaker

Prof. Junlong Chen, Fellow of the IEEE

South China University of Technology, China

陈俊龙(C. L. Philip Chen)博士是华南理工大学特聘讲席教授,博导,计算机科学与工程学院院长;欧洲科学院外籍院士,欧洲科学与艺术学院院士,中国自动化学会副理事长,曾任澳门大学科技学院院长,美国美国德州大学工学院终身教授,工学院副院长及电机计算机系主任。陈教授是IEEE Fellow,美国科学促进会AAAS Fellow,IAPR Fellow,香港工程师学会 Fellow,我国自动化学会的Fellow及常务理事。陈教授现任IEEE Transactions on Cybernetics 期刊主编,曾任 IEEE Transactions on Systems, Man, and Cybernetics: Systems 期刊主编 (2014-2019)。陈教授曾任该学会国际总主席(President,2012-2013),担任IEEE SMC学会 IEEE Fellow 的评审委员主席,常务理事及杰出演讲师。 陈教授在国际重要学术刊物上发表论文500余篇,其中SCI文章400余篇(300余篇在IEEE Transactions),在Web of Science 他人引用17000余次,谷歌学术引用30000余次。2016年他获得了母校—美国普度大学的杰出电机及计算机工程奖(Outstanding Electrical and Computer Engineering Award)。2018年获 IEEE系统人机控制论的最高学术奖--IEEE 诺伯特·维纳奖(Norbert Wiener Award)。2018、2019、2020连续三年入选科瑞唯安(Clarivate)全球高被引科学家。

Prof. Jinde Cao, Fellow of the IEEE

Southeast University, China

Jinde Cao (Fellow, IEEE) received the B.S. degree from Anhui Normal University, Wuhu, China, the M.S. degree from Yunnan University, Kunming, China, and the Ph.D. degree from Sichuan University, Chengdu, China, all in mathematics/applied mathematics, in 1986, 1989, and 1998, respectively. He was a Postdoctoral Research Fellow at the Department of Automation and Computer-Aided Engineering, Chinese University of Hong Kong, Hong Kong, from 2001 to 2002. He is an Endowed Chair Professor, the Dean of the School of Mathematics and the Director of the Research Center for Complex Systems and Network Sciences at Southeast University (SEU). He is also the Director of the National Center for Applied Mathematics at SEU-Jiangsu of China and the Director of the Jiangsu Provincial Key Laboratory of Networked Collective Intelligence of China. Dr. Cao was a recipient of the National Innovation Award of China, Obada Prize and the Highly Cited Researcher Award in Engineering, Computer Science, and Mathematics by Clarivate Analytics. He is elected as a member of Russian Academy of Sciences, a member of the Academy of Europe, a member of Russian Academy of Engineering, a member of the European Academy of Sciences and Arts, a member of the Lithuanian Academy of Sciences, a fellow of African Academy of Sciences, and a fellow of Pakistan Academy of Sciences.

Prof. Zhongfeng Wang, Fellow of the IEEE

Nanjing University, China

Speech Title: Challenges of AI Computing in the New Era and Research on Transformer Hardware Acceleration

王中风博士,国家特聘专家、IEEE Fellow和AAIA Fellow,VLSI Signal Processing领域国际著名专家。获清华大学学士、硕士及明尼苏达大学博士学位。先后任职于美国国家半导体公司、俄勒冈州立大学和博通公司,历任博通高级主任科学家和技术总监,现任南京大学特聘教授和微电子学院副院长。累计参与十余款商用芯片设计,总产值超百亿元。拥有数十项发明专明,发表350余篇国际期刊和会议论文,7次荣获IEEE主流会议和会刊的年度最佳论文奖。多次担任IEEE不同会刊的编委和客座编辑,数十次担任各种国际会议的技术委员和各类主席。有关技术方案已经被近20种网络通信国际标准采纳。

Dr. Zhongfeng Wang received both the B.E. and M.S. degrees in the Dept. of Automation at Tsinghua University, Beijing, China, in 1988 and 1990, respectively. He obtained the Ph.D. degree from the University of Minnesota, Minneapolis, in 2000. He has been working for Nanjing University, China, as a Distinguished Professor since 2016. Previously he worked for Broadcom Corporation, California, from 2007 to 2016 as a leading VLSI architect. Before that, he worked for Oregon State University and National Semiconductor Corporation.
Dr. Wang is a world-recognized expert on Low-Power High-Speed VLSI Design for Signal Processing Systems. He has published over 350 technical papers with multiple best paper awards received from the IEEE technical societies, among which is the VLSI Transactions Best Paper Award of 2007. He has edited one book VLSI and held more than 20 U.S. and China patents. In the current record, he has had many papers ranking among top 25 most (annually) downloaded manuscripts in IEEE Trans. on VLSI Systems. In the past, he has served as Associate Editor for IEEE Trans. on TCAS-I, T-CAS-II, and T-VLSI for many terms. He has also served as TPC member and various chairs for tens of international conferences. Moreover, he has contributed significantly to the industrial standards. So far, his technical proposals have been adopted by nearly 20 international networking standards. In 2015, he was elevated to the Fellow of IEEE for contributions to VLSI design and implementation of FEC coding. His current research interests are in the area of Optimized VLSI Design for Digital Communications and Deep Learning.

Speech Title: Challenges of LLM Computing and Research on Transformer Hardware Acceleration

Abstract: During the evolution of AI models, from multilayer perceptrons to large language models (LLMs) like GPT, the computational scale has become increasingly massive, and the functionality of LLMs has become more versatile. The LLMs play a significant role in various application scenarios such as visual processing and text generation. Among them, the transformer serves as a core component of them. This talk introduces the application prospects and the current state of research development in the field of LLM. It analyzes the main challenges faced by the research society in terms of transmission, storage, and computation. Based on optimization approaches such as sparsity, quantization, and in-memory computing, we present high-energy-efficient and low-complexity hardware design solutions in transformer acceleration from our research team. The related research can be applied to accelerating the inference and training of transformers. Additionally, this talk analyzes the key issues in the practical implementation of large language model applications and the mainstream trends in future AI computation.

Prof. Zhiwei Xu

University of Chinese Academy of Sciences, China

徐志伟,现任中国科学院计算技术研究所研究员、学术委员会主任,中国科学院大学岗位教授。1982年获电子科技大学学士学位,1984年获美国普度大学硕士学位,1987年获美国南加州大学博士学位。长期从事高性能计算体系结构与分布式系统研究。历任曙光信息产业有限公司总工程师,中科院计算所研究员、副所长、总工程师、学位委员会主任、学术委员会主任。出版《Scalable Parallel Computing》、《计算机科学导论》等著作多部。提出了普惠计算、人机物三元计算、算礼等学术思想,向国际社区贡献了大数据云计算开源软件。曾获得国家杰出青年科学基金、国家科技进步奖、中国计算机学会王选奖。

Speech Title: Computing and Communication: A New Era and Two Opportunities

Abstract: Two events happened in 2023: (1) the 3-year pandemic passed, and (2) the world entered a new era of human-cyber-physical computing and communication. This talk presents three salient features of this new era, and discusses its new research and educational opportunities.

Prof. Dusit Niyato, Fellow of the IEEE

Nanyang Technological University, Singapore

Speech Title: Optimizing Mobile-Edge AI-Generated Everything (AIGX) Services by Prompt Engineering

Abstract: As the next-generation paradigm for content creation, AI-Generated Content (AIGC), i.e., generating content automatically by Generative AI (GAI) based on user prompts, has gained great attention and success recently. With the ever-increasing power of GAI, especially the emergence of Pretrained Foundation Models (PFMs) that contain billions of parameters and prompt engineering methods (i.e., finding the best prompts for the given task), the application range of AIGC is rapidly expanding, covering various forms of information for human, systems, and networks, such as network designs, channel coding, and optimization solutions. In this presentation, we introduce the concept of mobile-edge AI-Generated Everything (AIGX). We first review the building blocks of AIGX, the evolution from AIGC to AIGX, as well as practical AIGX applications. Then, we present a unified mobile-edge AIGX framework, which employs edge devices to provide PFM-empowered AIGX services and optimizes such services via prompt engineering. More importantly, we demonstrate that suboptimal prompts lead to poor generation quality, which adversely affects user satisfaction, edge network performance, and resource utilization. Accordingly, we review a case study, showcasing how to train an effective prompt optimizer using ChatGPT and investigating how much improvement is possible with prompt engineering in terms of user experience, quality of generation, and network performance.

Dusit Niyato is currently a President's Chair Professor in Computer Science and Engineering in the School of Computer Science and Engineering, Nanyang Technological University, Singapore. He received B.E. from King Mongkuk’s Institute of Technology Ladkrabang (KMITL), Thailand in 1999 and Ph.D. in Electrical and Computer Engineering from the University of Manitoba, Canada in 2008. Dusit's research interests are in the areas of distributed collaborative machine learning, Internet of Things (IoT), edge intelligent generative AI and AI-generated content (AIGC), mobile and distributed computing, and wireless networks. Currently, Dusit is serving as Editor-in-Chief of IEEE Communications Surveys and Tutorials (impact factor of 35.6 for 2023), an area editor of IEEE Transactions on Vehicular Technology, editor of IEEE Transactions on Wireless Communications, associate editor of IEEE Internet of Things Journal, IEEE Transactions on Mobile Computing, IEEE Wireless Communications, IEEE Network, IEEE Transactions on Information Forensics and Security (TIFS), and ACM Computing Surveys. He was a guest editor of IEEE Journal on Selected Areas on Communications. He is the Members-at-Large to the Board of Governors of IEEE Communications Society for 2024-2026, and was a Distinguished Lecturer of the IEEE Communications Society for 2016-2017. He was named the 2017-2022 highly cited researcher in computer science. He is a Fellow of IEEE and a Fellow of IET.

Prof. Linglong Dai, Fellow of the IEEE

Tsinghua University, China

Linglong Dai (IEEE Fellow) is a Professor of the Department of Electronic Engineering, Tsinghua University, Beijing, China. His research area is transmission theory and technology for wireless communications, with the research topics including massive MIMO, reconfigurable intelligent surface (RIS), millimeter-wave and terahertz communications, machine learning for wireless communications, and electromagnetic information theory (EIT). He has published over 100 IEEE journal papers and over 60 IEEE conference papers. He also holds 21 granted patents. He co-authored the book “mmWave Massive MIMO: A Paradigm for 5G” (Academic Press, Elsevier, 2016). He has received 6 IEEE conference Best Paper Awards. He has also received the Electronics Letters Best Paper Award in 2016, the Outstanding Young Scholar of NSFC in 2017, the IEEE ComSoc Asia-Pacific Outstanding Young Researcher Award in 2017, the IEEE ComSoc Leonard G. Abraham Prize in 2020, the IEEE ComSoc Stephen O. Rice Prize in 2022, the IEEE ICC Outstanding Demo Award in 2022, and the Distinguished Young Scholar of NSFC in 2023. He was listed as a Highly Cited Researcher by Clarivate from 2020 to 2023.

Speech Title: 6G Wireless Communications: Vision, Enabling Technologies and New Perspectives

Abstract: To enable emerging applications such as holographic video, digital twins, virtual reality, etc., a lot of efforts have been dedicated to the advancement of future 6G wireless communications towards 2030. In June 2023, the ITU-R reached a consensus on the recommendation on the framework and objectives for the next generation of wireless communications, marking a significant milestone in 6G development. In this talk, we aim to unfold the visions, enabling technologies, and new perspectives of 6G wireless communications. Specifically, this talk will commence with a brief history overview of wireless communications. Subsequently, the usage scenarios, enabling technologies, and recent research progress of 6G will be introduced, encompassing the air-interface technologies (e.g., ultra-massive MIMO, reconfigurable intelligent surfaces, THz communications, wireless AI, ISAC, semantic communications, etc.) and network technologies (e.g., non-terrestrial networks, computing networks, etc.). Finally, we will explore the potential benefits of leveraging electromagnetic characteristics to empower new perspectives of 6G, such as electromagnetic information theory (EIT), near-field location division multiple access (LDMA), and holographic RIS.

Prof. Yutaka Ishibashi

Nagoya Institute of Technology, Japan

Yutaka Ishibashi received the B.E., M.E., and Ph.D. degrees from Nagoya Institute of Technology, Nagoya, Japan, in 1981, 1983, and 1990, respectively. In 1983, he joined the Musashino Electrical Communication Laboratory of Nippon Telegraph and Telephone Public Corporation (currently, NTT). From 1993 to 2001, he served as an Associate Professor of Department of Electrical and Computer Engineering, Faculty of Engineering, Nagoya Institute of Technology. Currently, he is a Professor of Graduate School of Engineering, Nagoya Institute of Technology. From June 2000 to March 2001, he was a visiting researcher, Department of Computer Science and Engineering, University of South Florida (USF), USA. He was the Head of Department of Computer Science, Faculty of Engineering, Nagoya Institute of Technology from 2005 to 2006, and the Head of Department of Computer Science, Graduate School of Engineering, Nagoya Institute of Technology from 2007 to 2009. He was also a College Director at Nagoya Institute of Technology from 2016 to 2020. His research interests include multisensory communications, QoS (Quality of Service) control, and remote robot control with force feedback.

He was the Chair of the IEICE Communication Quality Technical Committee from 2007 to 2009. He served as TPC Chair of IEEE CQR (Communications Quality and Reliability) Workshop in 2011 and 2012. He also served as NetGames (Network and Systems Support for Games) Workshop Co-Chair in 2006, 2010, 2014, and 2017, Executive Committee Chair of Tokai-Section Joint Conference on Electrical, Electronics, Information, and Related Engineering in Japan, Chair of IEEE MAW 2017 (Metro Area Workshop in Nagoya, 2017), Conference Co-Chairs of ICCC 2017 - 2023, Conference Chair of ICCCS 2018, TPC Chair of IEEE ICCE-TW 2018, Conference Co-Chairs of ICFCC 2019, 2020, ICCET 2019, WSCE 2019 - 2023, and ICCCI 2020 - 2023. He was IEEE Nagoya Section Chair in 2017 and 2018, ITE (The Institute of Image Information and Television Engineers) Vice President in 2020 through 2022, ITE Tokai Branch Chair in 2020 and 2021, and IPSJ (Information Processing Society of Japan) Tokai Branch Chair. He is a Fellow of IEICE, a Senior member of IEEE and IPSJ, and a Member of ACM, ITE, VRSJ, and IEEJ.

Speech Title: Early Detection, Prevention, and Recovery of/from Frailty Using Multisensory Information and Communications Technology

Speech Title: Toward realizing age-free society, in which elderly people feel lively, we need to support early detection, prevention, and recovery of/from frailty (i.e., vulnerable states of mental and physical functions) efficiently. It is imperative for us to solve the problem of sudden increase in demand of medical treatment and nursing care by reducing periods required for the treatment and care. We also need to work out the issue of workforce shortage owing to rapid aging and very low birthrate. In such a situation, we are currently studying and developing systems and devices which efficiently support early detection, prevention, and recovery of/from frailty in “Knowledge Hub Aichi,” Priority Research Project IV from Aichi Prefectural Government. In this keynote speech, we introduce our research supported by the project, in which we are researching and developing seven systems/devices as follows: (1) decision system of face concentration ratio, (2) classification and tailor-made system, (3) prevention and monitoring system, (4) walking support system by metaverse, (5) remote inspection and rehabilitation system, (6) finger devices, and (7) walking support devices. This research can make contribution to longevity society.

 

 

 

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