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

소자 디비젼 

나노 전자소자, 유연 소자 및 디스플레이, 초고속 전자소자, 에너지/바이오 메디컬 응용소자 분야를 중점적으로 연구하고 있다.
세부 연구분야로는 나노 CMOS 소자, 뉴로모픽 소자, 그래핀/2차원 반도체 소자, 유기 발광다이오드, 유연 디스플레이, 멤스 (MEMS), 밀리미터/테라헤르츠 소자, 화합물 반도체 및 3차원 집적소자, 바이오 메디컬 소자, 열전/태양전지 등 에너지소자 및 양자역학 기반 소자 시뮬레이션 등이 있다. 다양한 소자 및 시스템 연구를 통해 과학기술적, 산업적 파급 효과가 큰 차세대 기술을 추구하고 있다. 

Recent AI-related activities in Circuit Division

Specific ongoing research topics related to AI and machine learning within Circuit 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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