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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.
Know More

Latest News

Hojeong Kim, Selim An, Jaewoo Gwak, Sehyeon Park, Minsang Kim joined CELL

Hojeong Kim, Selim An, Jaewoo Gwak, Sehyeon Park , Minsang Kim joined CELL in the first semester of 2025. Welcome!

Accepted Paper at ICCD 2024

Research on efficient forward-only training for Hyperdimensional Computing, introducing SEP and IMP for better encoding adaptability and accuracy, will be presented at ICCD 2024.

Published in ACM TODAES

Research on improving Hyperdimensional Computing with trainable encoding and adaptive training is published in ACM Transactions on Design Automation of Electronic Systems (TODAES).

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