ABSTRACT: While the computing world is reaching the limits of Moore's Law, demands for performance in excess of 1 million trillion floating-point operations per second (1 exaflops) are arising from novel paradigms to address applications in big data, machine learning and analytics from nearly countless resources. This computing challenge has never been as complex or as critically important as it is now. We are witnessing a new computing era calling for a migration from an algorithmic based compute world to a machine-learning based, data-intensive, memory-centric computing paradigm in which human capabilities are scaled and magnified. This paradigm shift is driven by the abundance of data (big data era) and the computing resources required to process it in application spaces spanning mobile/embedded computing, cloud/edge computing, and the internet of things. This new trend has reinvigorated computer architecture research. Researchers are considering new ways forward based on machine learning.

In this talk, I will first briefly describe the new mega trends in computer architecture research. I will then present our own research efforts and introduce a design paradigm that exploits the use of machine-learning to simultaneously improve power-efficiency, performance, reliability and security, for heterogeneous multicore architectures and on-chip communications. The aim is to provide insights and directions of integrating architectural innovations with machine learning in a holistic approach for future computer architecture research.

BIO: Dr. Ahmed Louri is the David and Marilyn Karlgaard Endowed Chair Professor of Electrical and Computer Engineering at the George Washington University, which he joined in August 2015. He is also the director of the High Performance Computing Architectures and Technologies Laboratory. Dr. Louri received the Ph.D. degree in Computer Engineering from the University of Southern California, Los Angeles, California in 1988. From 1988 to 2015, he was a professor of Electrical and Computer Engineering at the University of Arizona, and during that time, he served six years (2000 to 2006) as the Chair of the Computer Engineering Program. Throughout his career, he has held invited visiting scientist positions and served as a research fellow at various institutions, including the University of Electro-Communications, Chofu, Japan; the Communications Research Laboratory, Tokyo, Japan; the Laboratoire d'Informatique du Parallelism, Lyon, France; the University of Tsukuba, Tsukuba, Japan; the University of Paul Sabatier, Toulouse, France; and the Centre Nationale de Recherche Scientifique (CNRS), Toulouse, France. From 2010 to 2013, Dr. Louri served as a program director in the National Science Foundation's (NSF) Directorate for Computer and Information Science and Engineering. He directed the core computer architecture program and was on the management team of several cross-cutting programs, including: Cyber-Physical Systems; Expeditions in Computing; Computing Research Infrastructure; Secure and Trustworthy Cyberspace; Failure-Resistant Systems, Science Engineering and Education for Sustainability; and Cyber-Discovery Initiative, among others.

Dr. Louri conducts research in the broad area of computer architecture and parallel computing, with emphasis on interconnection networks, optical interconnects for scalable parallel computing systems, reconfigurable computing systems, and power-efficient and reliable Network-on-Chips (NoCs) for multicore architectures. Recently he has been concentrating on: energy-efficient, reliable, and high-performance many-core architectures; accelerator-rich reconfigurable heterogeneous architectures; machine learning techniques for efficient computing, memory, and interconnect systems; emerging interconnect technologies (photonic, wireless, RF, hybrid) for NoCs; future parallel computing models and architectures (including convolutional neural networks, deep neural networks, and approximate computing); and cloud-computing and data centers. He has published more than 160 refereed journal articles and peer-reviewed conference papers, and is the co-inventor on several US and international patents.

Dr. Louri is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), a member of IEEE Computer Society (CS) Technical Committee on Computer Architecture, the IEEE CS Technical Committee on Parallel Processing, the IEEE CS Technical Committee on Microprocessors & Microcomputers, and the Optical Society of America. He is the recipient of the highly competitive and prestigious NSF Research Initiation Award (now called the NSF CAREER Award), the Advanced Telecommunications Organization of Japan Fellowship, the CNRS Research Excellence Fellowship, the Japan Society for the Promotion of Science Fellowship, and the NSF Outstanding Service Award in recognition of his outstanding service to the field of computing and the research community, as well as several teaching awards.

He served as a general chair for the 25th IEEE CS Annual Symposium of the High Performance Computer Architecture (HPCA-25, 2019), the 13th IEEE CS Annual Symposium of the High Performance Computer Architecture (HPCA-13, 2007), the general co-chair of the Second Workshop on Optics in Communications and Computer Sciences (1999); and the general chair for the Workshop on Optics in High-Performance Computing Systems (1996). He is the 2019 area chair of the 34th IEEE International Parallel and Distributed Processing Symposium (IPDPS 2020).

Dr. Louri is the Editor-in-Chief of the IEEE Transactions on Computers, the flagship journal for the IEEE Computer Society. He is also currently serving as associate editor for the IEEE Transaction on Sustainable Computing. He previously served on the editorial boards for IEEE Transactions on Computers, IEEE Transactions on Emerging Technology for Computing, and Cluster Computing, the Journal of Networks, Software Tools and Applications. Dr. Louri's recent IEEE Computer Society committee service includes being: chair for the IEEE CS Fellow Evaluation Committee (2019), an evaluator on the IEEE CS Fellow Evaluation Committee (2012), vice-chair for the IEEE CS Fellow Evaluation Committee (2013, 2017, and 2018), and a member of IEEE CS Computer Entrepreneur Award Committee (2017).

ABSTRACT: Nowadays, deep learning techniques have achieved amazing success in numerous applications. Especially, they are being increasingly adopted in various real-world applications (e.g. autonomous vehicles, rescue robots, drones, smart home, IoT systems) to learn models from the raw data aggregated by edge devices, hence, providing accurate prediction and decision-making. Traditionally, due to its huge compute power and scalability, the cloud data center is often the best option for training and evaluating AI applications. With the increasing computing power and energy efficiency of mobile and edge devices, there is a growing interest in performing AI applications on these platforms. As a result, we believe the next-generation AI applications are pervasive across all platforms, ranging from central cloud data center to edge-side wearable and mobile devices. However, we observe several gaps that challenge the pervasive AI applications. First, the large size of such newly developed AI networks poses both throughput and energy challenges to the underlying processing hardware, which hinders ubiquitous deployment for many promising AI applications. Second, the traditional statically trained AI model in cloud data center could not efficiently handle the dynamic data in the real in-situ environments, which leads to low inference accuracy. Lastly, the training of AI models still involves extensive human efforts to collect and label the large-scale dataset, which becomes impractical in big data era where raw data is largely un-labeled and uncategorized. In this talk, I will present architecture and system support which enables next generation AI applications to become high efficient and intelligent. I will first introduce Pervasive AI, a user satisfaction-aware deep learning inference framework, to provide the best user satisfaction when migrating AI-based applications from Cloud to all kinds of platforms. Next, I will describe In-situ AI, a novel-computing paradigm tailored to in-situ AI applications. Furthermore, to tackle the big data challenge and achieve real intelligent (support autonomous learning), I will introduce Unsupervised AI, an unsupervised GAN-based deep learning accelerator.

BIO: Dr. Tao Li is a full professor in the Department of Electrical and Computer Engineering at the University of Florida. He received a Ph.D. in Computer Engineering from the University of Texas at Austin. His research interests include computer architecture, microprocessor/memory/storage system design, virtualization technologies, energy-efficient/sustainable/ dependable data center, cloud/big data computing platforms, the impacts of emerging technologies/applications on computing, and evaluation of computer systems. Dr. Tao Li received 2009 National Science Foundation Faculty Early CAREER Award, 2008, 2007, 2006 IBM Faculty Awards, 2008 Microsoft Research Safe and Scalable Multi-core Computing Award and 2006 Microsoft Research Trustworthy Computing Curriculum Award. Dr. Tao Li co-authored two papers that won the Best Paper Awards in ICCD 2016, HPCA 2011 and seven papers that were nominated for the Best Paper Awards in HPCA 2018, HPCA 2017, ICPP 2015, CGO 2014, DSN 2011, MICRO 2008 and MASCOTS 2006. Dr. Tao Li is one of the College of Engineering winners, University of Florida Doctor Dissertation Advisor/Mentoring Award for 2013-2014 and 2011-2012.?Dr. Tao Li is an IEEE Fellow.

ABSTRACT:

BIO: Dr. Xin Yao is a part-time Professor of Computer Science in the School of Computer Science at the University of Birmingham and the Director of?the Centre of Excellence for Research in Computational Intelligence and Applications (CERCIA). He took up a chair professorship at the Department of Computer Science and Engineering, Southern University of Science and Technology (SUSTech), Shenzhen, China, in Fall 2016. He is also a?Fellow?of?IEEE, a Distinguished Lecturer, and a Past (2014-15) President of the?IEEE?Computational Intelligence Society. He was a Distinguished Visiting Professor at the?Nature Inspired Computation and Applications Laboratory (NICAL), USTC-Birmingham Joint Research Institute in Intelligent Computation and Its Applications,?University of Science and Technology of China, Hefei, China.

ABSTRACT: Blockchain is the fascinating distributed ledger technology, which holds out the promise of disintermediation, transparency, and openness. An increasing number of businesses, academics and even governments are starting to view blockchain systems as the cornerstone of trust the Web 3.0 era (next generation value Internet). This presentation will first trace the source and the current development status of blockchain systems in various application areas. Secondly, a roadmap of the major theoretical and practical challenging issues faced by these blockchain systems will be laid out. Finally, I will give a glimpse of harnessing the super-abundant opportunities of blockchain systems in the future landscape.

BIO: Hai Jin received his PhD degree in computer engineering from Huazhong University of Science and Technology, in 1994. He received German Academic Exchange Service fellowship to visit the Technical University of Chemnitz in Germany In 1996. He worked at the University of Hong Kong between 1998 and 2000, and as a visiting scholar at the University of Southern California between 1999 and 2000. He received the Excellent Youth Award from the National Science Foundation of China in 2001. He is a Cheung Kung Scholars chair professor of computer science and engineering of Huazhong University of Science and Technology, the chief scientist of ChinaGrid, the largest grid computing project in China, and the chief scientist of National 973 Basic Research Program Project of Virtualization Technology of Computing System, and Cloud Security. He has coauthored 22 books and published over 800 research papers. His research interests include computer architecture, irtualization technology, cluster computing and cloud computing, peer-to-peer computing, network storage, and network security. He is a fellow of CCF, a fellow of IEEE and a member of ACM.