Prof. Eunji Kwon - Kookmin University AI·SW Graduate School

Research Overview연구 개요

Research focuses on energy-efficient AI computing, including deep learning hardware accelerators, SW/HW co-optimization, and power management on mobile systems. Key topics include vision transformer acceleration, neural network quantization, and sparsity optimization.

에너지 효율적 AI 컴퓨팅을 연구하며, 딥러닝 하드웨어 가속기, SW/HW 공동 최적화, 모바일 시스템 전력 관리 등을 포함합니다. 주요 연구 주제로는 비전 트랜스포머 가속, 신경망 양자화, 희소성 최적화가 있습니다.

Research Areas연구 분야

Energy-Efficient AI Computing
Deep Learning Hardware Accelerators
SW/HW Co-optimization
Neural Network Quantization
Sparsity Optimization

에너지 효율적 AI 컴퓨팅
딥러닝 하드웨어 가속기
SW/HW 공동 최적화
신경망 양자화
희소성 최적화

Major Achievements주요 연구 성과

Mobile Transformer Accelerator with line sparsity and dynamic quantization (TCAD 2023)
RL-based mixed precision quantization for hybrid vision transformers (DAC 2024)

Recent Publications주요 논문

E. Kwon and T. Rosing, "Autonomous Model Quantization Framework for Hybrid Vision Transformers based on Reinforcement Learning," IEEE TCAD, 2026
E. Kwon, "TACo: Training-Free, Hardware-Aware ViT Architecture Search with a Hypervolume-Based Unified Zero-Cost Score," DAC, 2026
J. Nam, J. Kim, E. Kwon, and S. Kang, "Efficient Down-sampling in Hybrid Neural Network using Adversarial Autoencoders," DATE, 2026
S. Moon and E. Kwon, "DeltaTrack: Flow-Driven Multiple Object Tracking Accelerator with Variable LSB Approximation," IEEE TCAS-II, 2025
E. Kwon, M. Zhou, W. Xu, T. Losing, and S. Kang, "RL-PTQ: RL-based Mixed Precision Quantization for Hybrid Vision Transformers," DAC, 2024
E. Kwon, J. Yoon, and S. Kang, "Mobile Transformer Accelerator Exploiting Various Line Sparsity and Tile-based Dynamic Quantization," IEEE TCAD, 2024
S. Lee, K. Cho, E. Kwon, S. Park, S. Kim, and S. Kang, "ViT-ToGo: Vision Transformer Accelerator with Grouped Token Pruning," DATE, 2024
E. Kwon, H. Song, J. Park, and S. Kang, "Mobile Accelerator Exploiting Sparsity of Multi-Heads, Lines, and Blocks in Transformers," DATE, 2023
H. Song, J. Yoon, D. Kim, E. Kwon, and S. Kang, "FPGA-Based Accelerator for Rank-Enhanced and Highly-Pruned Block-Circulant Neural Networks," DATE, 2023
A. Kim, S. Lee, E. Kwon, and S. Kang, "Adaptive FSP: Adaptive Architecture Search with Filter Shape Pruning," ACCV, 2022
E. Kwon, S. Han, Y. Park, J. Yoon, and S. Kang, "Reinforcement Learning-based Power Management Policy for Mobile Device Systems," IEEE TCAS-I, 2021
Y. Kang, E. Kwon, S. Lee, Y. Byun, Y. Lee, and S. Kang, "Approach to Improve the Performance using Bit-level Sparsity in Neural Networks," DATE, 2021