Machine learning and deep neural networks have revolutionized various fields, most obvious examples are computer vision and natural language processing. Apart from the surging sizes of sophisticated models, an emerging trend is to go down the opposite route of deploying lightweight models on the edge (terminal or user end) for relatively simple AI tasks. This is named edge AI which is often constrained to run under restrictive compute and storage resources. In this talk, we will explore the latest theory in neural network modeling that allows the total avoidance of AI training that used to be slow, daunting or even impossible for the edge. Specifically, we will scratch the surface of the neural tangent kernel, and try to establish (well…. qualitatively) the equivalence of data and network, such that once the data are ready, the network is instantly ready, too.
Dr. Ngai Wong is currently an associate professor in the Department of Electrical and Electronic Engineering, HKU. His research spans electronic design automation, model order reduction, tensor algebra, system identification, and lately in neural network design and compression. Starting from late 2022, he will be coordinating a 5-year Theme-based Research Scheme with a team of local and overseas colleagues to look into the design and implementation of edge AI leveraging the emerging, next-generation microelectronics platform.
Clean water and clean air are vital for public health. This project focuses on developing high-efficiency and environmentally sustainable filters for removing harmful air/water pollutants. The team has developed novel architectures and functionalities for the filters to achieve high permeance, high removal efficiency, and excellent reusability.
