Computation Efficient Learning Lab.

EE Department @ POSTECH

We design the future learning technology and platform.

Our Research

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

Accepted Paper at PACT 2025

Research on scalable RAG retrieval, presented in our paper “Bit-Level Semantics: Scalable RAG Retrieval with Neurosymbolic Hyperdimensional Computing,” will appear at PACT 2025.

Accepted Paper at DAC 2025, ISCA 2025, SIGMETRICS 2025

3 papers accepted : DiTTO, a novel diffusion-based framework for generating realistic, configurable, and diverse multi-device storage traces(DAC 2025); FlexNeRFer, an energy-efficient NeRF accelerator (ISCA 2025); a diffusion-based generative-AI surrogate

Continue ReadingAccepted Paper at DAC 2025, ISCA 2025, SIGMETRICS 2025

Accepted Paper at ICRA 2025, DATE 2025

3 papers accepted: Reserach on a dynamic-encoding, oversampling HD federated learning framework for mobile robots (ICRA 2025); an HD framework designed with fine-grained feature encoding and a robust training scheme

Continue ReadingAccepted Paper at ICRA 2025, DATE 2025

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

77 Cheongam-Ro, Namgu, Pohang, LG Research Building, Professor Room 301, Student Room 302 (Postal code: 37673), REPUBLIC OF KOREA

Email

yeseongkim@postech.ac.kr

Phone

(+82) 054-279-2218