AI in EE

AI IN DIVISIONS

Overview
AI and machine learning are
a key thrust in EE research
AI and machine learning are 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.

Device division

Nano electronics, flexible electronics and displays, high-speed electronics and energy/biomedical devices are the main topics of research. Interests include Nano-CMOS devices, neuromorphic devices, graphene/2D semiconductor devices, organic displays, flexible displays, MEMS, mm-wave/THz devices, compound semiconductors and 3D integration, biomedical/energy devices and quantum mechanical simulation for device. We are aiming to make significant technological and industrial impacts through a variety of research on electronic devices and related systems.

Recent AI-related activities in Device Division

Specific ongoing research topics related to AI and machine learning within Device Division of KAIST EE include:

AI and machine learning are a key thrust in EE research
In fact, 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.
Recent AI-related activities in Device Division
In recent years, artificial neural networks (ANN) including deep neural network (DNN) and spiking neural network (SNN) have achieved unprecedented accuracies in large-scale recognition and classification tasks by utilizing supercomputing resources. While several application-specific integrated circuit (ASIC) solutions utilizing conventional CMOS devices have been previously proposed, limitations still exist on energy consumptions, online learning capabilities and chip density. To address all issues in AI hardware, the community is moving towards utilizing emerging AI devices as artificial neurons and synapses because they can offer fast parallel computing at extremely small device footprint with low power consumption. The main research interest of Device Division of KAIST EE is to develop large-scale neural network arrays for artificial intelligence (AI) hardware based on new design of artificial neurons and synapses. Specific ongoing research topics related to AI and machine learning within Device Division of KAIST EE include:
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