Seongmin Hong

According to our database, Seongmin Hong authored at least 13 papers between 2017 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

Accelerating Deep Convolutional Neural Networks Using Number Theoretic Transform.

Prasetiyo, Seongmin Hong, Yashael Faith Arthanto, Joo-Young Kim

IEEE Trans. Circuits Syst. I Regul. Pap., January, 2023

On Exact Inversion of DPM-Solvers.

Seongmin Hong, Kyeonghyun Lee, Suh Yoon Jeon, Hyewon Bae, Se Young Chun

CoRR, 2023

On the Robustness of Normalizing Flows for Inverse Problems in Imaging.

Seongmin Hong, Inbum Park, Se Young Chun

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

HyperAccel Latency Processing Unit (LPU^TM) Accelerating Hyperscale Models for Generative AI.

Seungjae Moon, Junsoo Kim, Jung-Hoon Kim, Junseo Cha, Gyubin Choi, Seongmin Hong, Joo-Young Kim

Proceedings of the 35th IEEE Hot Chips Symposium, 2023

Neural Diffeomorphic Non-uniform B-spline Flows.

Seongmin Hong, Se Young Chun

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Dynamic Rate Neural Acceleration Using Multiprocessing Mode Support.

Inho Lee, Yangki Lee, Hongjun Um, Seongmin Hong, Yongjun Park

IEEE Trans. Very Large Scale Integr. Syst., 2022

DFX: A Low-latency Multi-FPGA Appliance for Accelerating Transformer-based Text Generation.

Seongmin Hong, Seungjae Moon, Junsoo Kim, Sungjae Lee, Minsub Kim, Dongsoo Lee, Joo-Young Kim

Proceedings of the 2022 IEEE Hot Chips 34 Symposium, 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, Sungjei Kim, Maciej Smyl, Tomasz Latkowski, Pawel Kubik, Michal Sokolski, Yujie Ma, Jiahao Chao, Zhou Zhou, Hongfan Gao, Zhengfeng Yang, Zhenbing Zeng, Zhengyang Zhuge, Chenghua Li, Dan Zhu, Mengdi Sun, Ran Duan, Yan Gao, Lingshun Kong, Long Sun, Xiang Li, Xingdong Zhang, Jiawei Zhang, Yaqi Wu, Jinshan Pan, Gaocheng Yu, Jin Zhang, Feng Zhang, Zhe Ma, Hongbin Wang, Hojin Cho, Steve Kim, Huaen Li, Yanbo Ma, Ziwei Luo, Youwei Li, Lei Yu, Zhihong Wen, Qi Wu, Haoqiang Fan, Shuaicheng Liu, Lize Zhang, Zhikai Zong, Jeremy Kwon, Junxi Zhang, Mengyuan Li, Nianxiang Fu, Guanchen Ding, Han Zhu, Zhenzhong Chen, Gen Li, Yuanfan Zhang, Lei Sun, Dafeng Zhang, Neo Yang, Fitz Liu, Jerry Zhao, Mustafa Ayazoglu, Bahri Batuhan Bilecen, Shota Hirose, Kasidis Arunruangsirilert, Luo Ao, Ho Chun Leung, Andrew Wei, Jie Liu, Qiang Liu, Dahai Yu, Ao Li, Lei Luo, Ce Zhu, Seongmin Hong, Dongwon Park, Joonhee Lee, Byeong Hyun Lee, Seunggyu Lee, Se Young Chun, Ruiyuan He, Xuhao Jiang, Haihang Ruan, Xinjian Zhang, Jing Liu, Garas Gendy, Nabil Sabor, Jingchao Hou, Guanghui He

Proceedings of the Computer Vision - ECCV 2022 Workshops, 2022

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, Zehui Chen, Jialei Xu, Xianming Liu, Junjun Jiang, XueChao Shi, Difan Xu, Yanan Li, Xiaotao Wang, Lei Lei, Ziyu Zhang, Yicheng Wang, Zilong Huang, Guozhong Luo, Gang Yu, Bin Fu, Jiaqi Li, Yiran Wang, Zihao Huang, Zhiguo Cao, Marcos V. Conde, Denis Sapozhnikov, Byeong Hyun Lee, Dongwon Park, Seongmin Hong, Joonhee Lee, Seunggyu Lee, Se Young Chun

Proceedings of the Computer Vision - ECCV 2022 Workshops, 2022

2021

FIXAR: A Fixed-Point Deep Reinforcement Learning Platform with Quantization-Aware Training and Adaptive Parallelism.

Je Yang, Seongmin Hong, Joo-Young Kim

Proceedings of the 58th ACM/IEEE Design Automation Conference, 2021

2018

Automated Neural Network Accelerator Generation Framework for Multiple Neural Network Applications.

Inho Lee, Seongmin Hong, Giha Ryu, Yongjun Park

Proceedings of the TENCON 2018, 2018

NN compactor: Minimizing memory and logic resources for small neural networks.

Seongmin Hong, Inho Lee, Yongjun Park

Proceedings of the 2018 Design, Automation & Test in Europe Conference & Exhibition, 2018

2017

A FPGA-based neural accelerator for small IoT devices.

Seongmin Hong, Yongjun Park

Proceedings of the International SoC Design Conference, 2017