Professor C.-C. Jay Kuo, Director of MCL, was invited to give a keynote at the AIxMM
conference held in Laguna Hills, California, USA, on February 4 (Tuesday). The title of
Professor Kuo’s keynote is “Mobile/Edge Visual Analytics via Green AI.” The abstract of
is given below.
“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.”
Professor Kuo received quite a few questions immediately after the talk. He also
participated a panel discussion in the afternoon, 1-2:30 pm.