Sung-En Chang

According to our database, Sung-En Chang authored at least 16 papers between 2017 and 2024.

Collaborative distances:

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

2024

Digital Avatars: Framework Development and Their Evaluation.

Timothy Rupprecht, Sung-En Chang, Yushu Wu, Lei Lu, Enfu Nan, Chih-hsiang Li, Caiyue Lai, Zhimin Li, Zhijun Hu, Yumei He, David R. Kaeli, Yanzhi Wang

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024

SDA: Low-Bit Stable Diffusion Acceleration on Edge FPGAs.

Geng Yang, Yanyue Xie, Zhong Jia Xue, Sung-En Chang, Yanyu Li, Peiyan Dong, Jie Lei, Weiying Xie, Yanzhi Wang, Xue Lin, Zhenman Fang

Proceedings of the 34th International Conference on Field-Programmable Logic and Applications, 2024

SuperFlow: A Fully-Customized RTL-to-GDS Design Automation Flow for Adiabatic Quantum- Flux - Parametron Superconducting Circuits.

Yanyue Xie, Peiyan Dong, Geng Yuan, Zhengang Li, Masoud Zabihi, Chao Wu, Sung-En Chang, Xufeng Zhang, Xue Lin, Caiwen Ding, Nobuyuki Yoshikawa, Olivia Chen, Yanzhi Wang

Proceedings of the Design, Automation & Test in Europe Conference & Exhibition, 2024

2023

ESRU: Extremely Low-Bit and Hardware-Efficient Stochastic Rounding Unit Design for Low-Bit DNN Training.

Sung-En Chang, Geng Yuan, Alec Lu, Mengshu Sun, Yanyu Li, Xiaolong Ma, Zhengang Li, Yanyue Xie, Minghai Qin, Xue Lin, Zhenman Fang, Yanzhi Wang

Proceedings of the Design, Automation & Test in Europe Conference & Exhibition, 2023

2022

FILM-QNN: Efficient FPGA Acceleration of Deep Neural Networks with Intra-Layer, Mixed-Precision Quantization.

Mengshu Sun, Zhengang Li, Alec Lu, Yanyu Li, Sung-En Chang, Xiaolong Ma, Xue Lin, Zhenman Fang

Proceedings of the FPGA '22: The 2022 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, Virtual Event, USA, 27 February 2022, 2022

You Already Have It: A Generator-Free Low-Precision DNN Training Framework Using Stochastic Rounding.

Geng Yuan, Sung-En Chang, Qing Jin, Alec Lu, Yanyu Li, Yushu Wu, Zhenglun Kong, Yanyue Xie, Peiyan Dong, Minghai Qin, Xiaolong Ma, Xulong Tang, Zhenman Fang, Yanzhi Wang

Proceedings of the Computer Vision - ECCV 2022, 2022

Hardware-efficient stochastic rounding unit design for DNN training: late breaking results.

Sung-En Chang, Geng Yuan, Alec Lu, Mengshu Sun, Yanyu Li, Xiaolong Ma, Zhengang Li, Yanyue Xie, Minghai Qin, Xue Lin, Zhenman Fang, Yanzhi Wang

Proceedings of the DAC '22: 59th ACM/IEEE Design Automation Conference, San Francisco, California, USA, July 10, 2022

Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm.

Shaoyi Huang, Dongkuan Xu, Ian En-Hsu Yen, Yijue Wang, Sung-En Chang, Bingbing Li, Shiyang Chen, Mimi Xie, Sanguthevar Rajasekaran, Hang Liu, Caiwen Ding

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2022

2021

ILMPQ : An Intra-Layer Multi-Precision Deep Neural Network Quantization framework for FPGA.

Sung-En Chang, Yanyu Li, Mengshu Sun, Yanzhi Wang, Xue Lin

CoRR, 2021

Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm.

Shaoyi Huang, Dongkuan Xu, Ian En-Hsu Yen, Sung-En Chang, Bingbing Li, Shiyang Chen, Mimi Xie, Hang Liu, Caiwen Ding

CoRR, 2021

RMSMP: A Novel Deep Neural Network Quantization Framework with Row-wise Mixed Schemes and Multiple Precisions.

Sung-En Chang, Yanyu Li, Mengshu Sun, Weiwen Jiang, Sijia Liu, Yanzhi Wang, Xue Lin

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

Mix and Match: A Novel FPGA-Centric Deep Neural Network Quantization Framework.

Sung-En Chang, Yanyu Li, Mengshu Sun, Runbin Shi, Hayden K. H. So, Xuehai Qian, Yanzhi Wang, Xue Lin

Proceedings of the IEEE International Symposium on High-Performance Computer Architecture, 2021

2020

MSP: An FPGA-Specific Mixed-Scheme, Multi-Precision Deep Neural Network Quantization Framework.

Sung-En Chang, Yanyu Li, Mengshu Sun, Weiwen Jiang, Runbin Shi, Xue Lin, Yanzhi Wang

CoRR, 2020

2018

Learning Tensor Latent Features.

Sung-En Chang, Xun Zheng, Ian En-Hsu Yen, Pradeep Ravikumar, Rose Yu

CoRR, 2018

MixLasso: Generalized Mixed Regression via Convex Atomic-Norm Regularization.

Ian En-Hsu Yen, Wei-Cheng Lee, Kai Zhong, Sung-En Chang, Pradeep Ravikumar, Shou-De Lin

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Latent Feature Lasso.

Ian En-Hsu Yen, Wei-Cheng Lee, Sung-En Chang, Arun Sai Suggala, Shou-De Lin, Pradeep Ravikumar

Proceedings of the 34th International Conference on Machine Learning, 2017