Title: Mobile Deep Neural Network Inference in Edge Computing
Abstract: Implementing Deep Neural Network (DNN) applications usually requires powerful computing resources to process a large amount of data. In the mobile edge computing environment, edge devices have limited capacity and the DNN application further suffers from issues of the wireless connection, e.g., handovers, service outage, etc. Without properly addressing these issues, the wider application of DNN in practice will seriously be hindered. In this talk, I will address several key challenging problems in effective deployment and efficient execution of DNN models in the mobile edge computing environment.
Bio: Dong Yuan is a senior lecturer from the School of Electrical and Information Engineering, the University of Sydney. He received the B. Eng. degree and M. Eng. degree from Shandong University, Jinan, China, in 2005 and 2008, the PhD degree from Swinburne University of Technology, Melbourne, Australia, in 2012, all in computer science. His research interests include cloud and edge computing, parallel and distributed systems, scheduling and resource management, Internet of things, big data and artificial intelligence. He has published over 80 papers in top journals and conferences including TPDS, TC, ToSEM, TIFS, ICDCS, IPDPS, MobiHoc, AAAI, CIKM, etc.