Mobile/Edge Visual Analytics via Green AI

Abstract: Mobile/edge visual analytics will prevail in the modern AI era. Most researchers focus on deep-learning-based model compression to achieve this goal. Model compression can reduce the model size by 50-80% with slight performance degradation. Model compression relies on an existing larger model. The training cost of such a large model remains. The compression step also demands resources. I have worked on green AI since 2014, published many papers on this topic, and coined this emerging field “green learning.” Green learning demands low power consumption in both training and inference. It has attractive characteristics, such as small model sizes, fewer training samples, mathematical transparency, ease of incremental learning, etc. It can reduce the model size of its deep-learning counterpart by 95-99%. The training can be conducted from scratch. The resulting model is inherently smaller. It is ideal for mobile and edge devices. Green learning relies on signal-processing disciplines such as filter banks, linear algebra, subspace learning, probability theory, etc. Although it exploits optimization, it avoids end-to-end system optimization, a non-convex optimization problem. Instead, it adopts modularized optimization, and each optimization problem can be cast as convex optimization. In this example, I will use several examples to demonstrate the advantages of green learning in visual analytics for mobile/edge devices.

Bio: Dr. C.-C. Jay Kuo received his Ph.D. from the Massachusetts Institute of Technology in 1987. He is now with the University of Southern California (USC) as the Ming Hsieh Chair Professor, a Distinguished Professor of Electrical and Computer Engineering and Computer Science, and the Director of the Media Communications Laboratory. His research interests are in visual computing and communication. He is a Fellow of AAAS, ACM, IEEE, NAI, and SPIE and an Academician of Academia Sinica. Dr. Kuo has received a few awards for his research contributions, including the 2010 Electronic Imaging Scientist of the Year Award, the 2010-11 Fulbright-Nokia Distinguished Chair in Information and Communications Technologies, the 2019 IEEE Computer Society Edward J. McCluskey Technical Achievement Award, the 2019 IEEE Signal Processing Society Claude Shannon-Harry Nyquist Technical Achievement Award, the 72nd annual Technology and Engineering Emmy Award (2020), and the 2021 IEEE Circuits and Systems Society Charles A. Desoer Technical Achievement Award. Dr. Kuo was the Editor-in-Chief of the IEEE Transactions on Information Forensics and Security (2012-2014) and the Journal of Visual Communication and Image Representation (1997-2011). He is currently the Editor-in-Chief for the APSIPA Trans. on Signal and Information Processing (2022-2023). He has guided 179 students to their Ph.D. degrees and supervised 31 postdoctoral research fellows.