AI in EE
AI IN DIVISIONS
a key thrust in EE research
AI/machine learning efforts are already a big part of ongoing research in all 6 divisions -
Computer, Communication, Signal, Wave, Circuit and Device - of KAIST EE. Examples include
neuromorphic devices, VLSI hardware architecture tailored to machine learning, image/voice recognition
via deep learning, statistical inference, coding and information theory to enhance distributed machine
learning, intelligent robots, quantum information, brain imaging, etc.
AI and machine learning continue to find new domains of applications every day. As a case in point, in the wave division research dealing with electromagnetic (EM) wave and quantum information, AI techniques are increasingly introduced in analysis, signal processing and subsystem designs for improving performance. A good example is to utilize AI machines to learn behaviors of EM physical systems in designing new subsystems for signal equalization and interference elimination, as done in neural network channel equalizer design for 10+ Gbps optical transmission channels. The wave division is also making critical efforts to extend the boundaries of the AI technology. Examples include memrister-based 3D IC design and new silicon technology geared to building next generation deep neural networks. Exciting new opportunities also exist in extending the capacity and capability of AI machines using the quantum technology. In particular, recent advances in quantum computing made possible by the superconductor quantum process unit (QPU) technology allow exploration of new quantum AI technologies.
See below for specific ongoing research topics related to AI and machine learning within the Wave Division of KAIST EE.