Edge-AI and Mission-critical Industrial Applications
Albert Y. Zomaya, Peter Nicol Russell Chair Professor , Fellow of AAAS, IEEE
Centre for Distributed and High-Performance Computing, University of Sydney
Albert Y. ZOMAYA is Peter Nicol Russell Chair Professor of Computer Science and Director of the Centre for Distributed and High-Performance Computing at the University of Sydney. To date, he has published > 700 scientific papers and articles and is (co-)author/editor of >30 books. A sought-after speaker, he has delivered >250 keynote addresses, invited seminars, and media briefings. He is currently the Editor in Chief of the ACM Computing Surveys and served in the past as Editor in Chief of the IEEE Transactions on Computers (2010-2014) and the IEEE Transactions on Sustainable Computing (2016-2020).
Professor Zomaya is a decorated scholar with numerous accolades including Fellowship of the IEEE, the American Association for the Advancement of Science, and the Institution of Engineering and Technology. He is a Fellow of the Royal Society of New South Wales, Foreign Member of Academia Europaea, and Member of the European Academy of Sciences and Arts. Some of Professor Zomaya recent awards include the New South Wales Premier’s Prize of Excellence in Engineering and Information and Communications Technology (2019) and the Research Innovation Award, IEEE Technical Committee on Cloud Computing (2021). His research interests lie in parallel and distributed computing, networking, and complex systems.
Abstract: In the past few decades, industrial automation has become a driving force in a wide range of industries. There is a broad agreement that the deployment of computing resources close to where data is created is more business-friendly, as it can address system latency, privacy, cost, and resiliency challenges that a pure cloud computing approach cannot address. This computing paradigm is now known as Edge Computing. Having said that, the full potential of this transformation for both of computing and data analytics is far from being realized. The industrial requirements are much more stringent than what a simple edge computing paradigm can deliver. This is particularly true when mission-critical industrial applications have strict requirements on real-time decision making, operational technology innovation, data privacy, and running environment. In this talk, I aim to provide a few answers by combining real-time computing strengths into modern data- and intelligence-rich computing ecosystems. I will also explore the topic of Edge AI, which is a process in which the Edge systems uses machine learning algorithms to process data generated by the user’s devices.