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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