Computation Efficient Learning Lab.

EECS and AI Department @ DGIST

We design the future learning technology and platform.

Our Research
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About Us
Primary Research Theme

Efficient AI Technology

We focus on how to redesign AI and learning technologies towards superior computing efficiency for IoT/Big Data/edge computing. We explore alternative computing solutions for future learning technology, including near data computing to push computation beyond traditional processors, and brain-inspired hyperdimensional computing that closely models the ultimate efficient processor - the human brain.
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Latest News

Chaitali Bhattacharyya joined CELL

Chaitali Bhattacharyya in the AI department joined CELL in the second semester of 2024. Welcome!

Accepted paper at ICRA 2024

Our recent collaboration with Coga Robotics on applying Hyperdimensional Computing to robotic sensorimotor controls has been accepted for presentation at the ICRA’24

Accepted paper at DATE 2024

Our research on developing a dynamic encoder for Hyperdimensional Computing, which significantly improves accuracy and efficiency, has been accepted for presentation at DATE’24

Active Research Topics

On-Device Generative AI

We advance state-of-the-art Generative AI by developing sophisticated models, including LLM and Diffusion, and applying them to emerging fields such as system optimization and performance evaluation. Our work emphasizes the development of techniques for model optimization and knowledge compression, facilitating efficient deployment across diverse application domains and driving innovation in modern system design.

Brain-inspired Hyperdimensional Computing

Hyperdimensional (HD) computing is an alternative computing method that processes cognitive tasks in a lightweight and error-torrent way based on theoretical neuroscience. We work on developing neuro-symbolic AI to support various learning tasks such as supervised, unsupervised, and reinforcement learning based on HD computing.

Learning with Alternative Computing

We rethink the role of machine learning in various alternative computing. We explore diverse system-level solutions to seamlessly accelerate AI applications on next-generation computer architectures such as near-data computing, in-memory computing, and CXL (Compute Express Link). We also develop ML-driven system software to enhance the capability of traditional computers.

Address

E3 Building Room 613, 333, Techno jungang-daero, Hyeonpung-myeon Dalseong-gun, Daegu, 42988, REPUBLIC OF KOREA

Email

yeseongkim(AT)dgist.ac.kr

Phone

(+82) 053-785-6332