A FPGA-based neural accelerator for small IoT devices. - Seongmin Hong; Yongjun Park 001 (ISOCC, 2017)

NN compactor: Minimizing memory and logic resources for small neural networks. - Seongmin Hong; Inho Lee 002; Yongjun Park 001 (DATE, 2018)

Automated Neural Network Accelerator Generation Framework for Multiple Neural Network Applications. - Inho Lee 002; Seongmin Hong; Giha Ryu; Yongjun Park 001 (TENCON, 2018)

FIXAR: A Fixed-Point Deep Reinforcement Learning Platform with Quantization-Aware Training and Adaptive Parallelism. - Je Yang; Seongmin Hong; Joo-Young Kim 001 (DAC, 2021)

Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Report. - Andrey Ignatov; Grigory Malivenko; Radu Timofte; Lukasz Treszczotko; Xin Chang; Piotr Ksiazek; Michal Lopuszynski; Maciej Pioro; Rafal Rudnicki; Maciej Smyl; Yujie Ma; Zhenyu Li 007; et al. (ECCV Workshops (3), 2022)

Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 Challenge: Report. - Andrey Ignatov; Radu Timofte; Maurizio Denna; Abdel Younes; Ganzorig Gankhuyag; Jingang Huh; Myeong Kyun Kim; Kihwan Yoon; Hyeon-Cheol Moon; Seungho Lee; Yoonsik Choe; Jinwoo Jeong; et al. (ECCV Workshops (3), 2022)

DFX: A Low-latency Multi-FPGA Appliance for Accelerating Transformer-based Text Generation. - Seongmin Hong; Seungjae Moon; Junsoo Kim 002; Sungjae Lee 002; Minsub Kim; Dongsoo Lee; Joo-Young Kim 001 (HCS, 2022)

Dynamic Rate Neural Acceleration Using Multiprocessing Mode Support. - Inho Lee 002; Yangki Lee; Hongjun Um; Seongmin Hong; Yongjun Park 001 (IEEE Trans. Very Large Scale Integr. Syst., 2022)

Neural Diffeomorphic Non-uniform B-spline Flows. - Seongmin Hong; Se Young Chun (AAAI, 2023)

HyperAccel Latency Processing Unit (LPUTM) Accelerating Hyperscale Models for Generative AI. - Seungjae Moon; Junsoo Kim 002; Jung-Hoon Kim; Junseo Cha; Gyubin Choi; Seongmin Hong; Joo-Young Kim 006 (HCS, 2023)

On the Robustness of Normalizing Flows for Inverse Problems in Imaging. - Seongmin Hong; Inbum Park; Se Young Chun (ICCV, 2023)

Accelerating Deep Convolutional Neural Networks Using Number Theoretic Transform. - Prasetiyo; Seongmin Hong; Yashael Faith Arthanto; Joo-Young Kim 001 (IEEE Trans. Circuits Syst. I Regul. Pap., 2023)

On Exact Inversion of DPM-Solvers. - Seongmin Hong; Kyeonghyun Lee; Suh Yoon Jeon; Hyewon Bae; Se Young Chun (CVPR, 2024)

Compressed Depth Map Super-Resolution and Restoration: AIM 2024 Challenge Results. - Marcos V. Conde; Florin-Alexandru Vasluianu; Jinhui Xiong; Wei Ye; Rakesh Ranjan; Radu Timofte; Huan Zheng; Wencheng Han; Tianyi Yan; Jianbing Shen; Pihai Sun; Yuanqi Yao; et al. (ECCV Workshops (10), 2024)

Adaptive Selection of Sampling-Reconstruction in Fourier Compressed Sensing. - Seongmin Hong; Jaehyeok Bae; Jongho Lee 003; Se Young Chun (ECCV (65), 2024)

Gradient-free Decoder Inversion in Latent Diffusion Models. - Seongmin Hong; Suh Yoon Jeon; Kyeonghyun Lee; Ernest K. Ryu; Se Young Chun (NeurIPS, 2024)

LPU: A Latency-Optimized and Highly Scalable Processor for Large Language Model Inference. - Seungjae Moon; Jung-Hoon Kim; Junsoo Kim 002; Seongmin Hong; Junseo Cha; Minsu Kim 004; Sukbin Lim; Gyubin Choi; Dongjin Seo; Jongho Kim; Hunjong Lee; Hyunjun Park; et al. (CoRR, 2024)

Adaptive Selection of Sampling-Reconstruction in Fourier Compressed Sensing. - Seongmin Hong; Jaehyeok Bae; Jongho Lee; Se Young Chun (CoRR, 2024)

A Latency Processing Unit: A Latency-Optimized and Highly Scalable Processor for Large Language Model Inference. - Seungjae Moon; Jung-Hoon Kim; Junsoo Kim 002; Seongmin Hong; Junseo Cha; Minsu Kim 004; Sukbin Lim; Gyubin Choi; Dongjin Seo; Jongho Kim; Hunjong Lee; Hyunjun Park; et al. (IEEE Micro, 2024)

Oaken: Fast and Efficient LLM Serving with Online-Offline Hybrid KV Cache Quantization. - Minsu Kim 004; Seongmin Hong; Ryeowook Ko; Soongyu Choi; Hunjong Lee; Junsoo Kim 002; Joo-Young Kim 001; Jongse Park (ISCA, 2025)

ADOR: A Design Exploration Framework for LLM Serving with Enhanced Latency and Throughput. - Junsoo Kim 002; Hunjong Lee; Geonwoo Ko; Gyubin Choi; Seri Ham; Seongmin Hong; Joo-Young Kim 001 (ISPASS, 2025)

ADOR: A Design Exploration Framework for LLM Serving with Enhanced Latency and Throughput. - Junsoo Kim 002; Hunjong Lee; Geonwoo Ko; Gyubin Choi; Seri Ham; Seongmin Hong; Joo-Young Kim 006 (CoRR, 2025)

Oaken: Fast and Efficient LLM Serving with Online-Offline Hybrid KV Cache Quantization. - Minsu Kim 004; Seongmin Hong; Ryeowook Ko; Soongyu Choi; Hunjong Lee; Junsoo Kim 002; Joo-Young Kim 006; Jongse Park (CoRR, 2025)