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Sung-En Chang
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2020 – today
- 2024
- [c12]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:
SuperFlow: A Fully-Customized RTL-to-GDS Design Automation Flow for Adiabatic Quantum- Flux - Parametron Superconducting Circuits. DATE 2024: 1-6 - [c11]Geng Yang, Yanyue Xie, Zhong Jia Xue, Sung-En Chang, Yanyu Li, Peiyan Dong, Jie Lei, Weiying Xie, Yanzhi Wang, Xue Lin, Zhenman Fang:
SDA: Low-Bit Stable Diffusion Acceleration on Edge FPGAs. FPL 2024: 264-273 - [c10]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:
Digital Avatars: Framework Development and Their Evaluation. IJCAI 2024: 8780-8783 - [i8]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:
SuperFlow: A Fully-Customized RTL-to-GDS Design Automation Flow for Adiabatic Quantum-Flux-Parametron Superconducting Circuits. CoRR abs/2407.18209 (2024) - [i7]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:
Digital Avatars: Framework Development and Their Evaluation. CoRR abs/2408.04068 (2024) - 2023
- [c9]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:
ESRU: Extremely Low-Bit and Hardware-Efficient Stochastic Rounding Unit Design for Low-Bit DNN Training. DATE 2023: 1-6 - 2022
- [c8]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:
Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm. ACL (1) 2022: 190-200 - [c7]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:
Hardware-efficient stochastic rounding unit design for DNN training: late breaking results. DAC 2022: 1396-1397 - [c6]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:
You Already Have It: A Generator-Free Low-Precision DNN Training Framework Using Stochastic Rounding. ECCV (12) 2022: 34-51 - [c5]Mengshu Sun, Zhengang Li, Alec Lu, Yanyu Li, Sung-En Chang, Xiaolong Ma, Xue Lin, Zhenman Fang:
FILM-QNN: Efficient FPGA Acceleration of Deep Neural Networks with Intra-Layer, Mixed-Precision Quantization. FPGA 2022: 134-145 - 2021
- [c4]Sung-En Chang, Yanyu Li, Mengshu Sun
Mix and Match: A Novel FPGA-Centric Deep Neural Network Quantization Framework. HPCA 2021: 208-220 - [c3]Sung-En Chang, Yanyu Li, Mengshu Sun
RMSMP: A Novel Deep Neural Network Quantization Framework with Row-wise Mixed Schemes and Multiple Precisions. ICCV 2021: 5231-5240 - [i6]Shaoyi Huang, Dongkuan Xu, Ian En-Hsu Yen, Sung-En Chang, Bingbing Li, Shiyang Chen, Mimi Xie, Hang Liu, Caiwen Ding:
Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm. CoRR abs/2110.08190 (2021) - [i5]Sung-En Chang, Yanyu Li, Mengshu Sun, Weiwen Jiang, Sijia Liu, Yanzhi Wang, Xue Lin:
RMSMP: A Novel Deep Neural Network Quantization Framework with Row-wise Mixed Schemes and Multiple Precisions. CoRR abs/2111.00153 (2021) - [i4]Sung-En Chang, Yanyu Li, Mengshu Sun, Yanzhi Wang, Xue Lin:
ILMPQ : An Intra-Layer Multi-Precision Deep Neural Network Quantization framework for FPGA. CoRR abs/2111.00155 (2021) - 2020
- [i3]Sung-En Chang, Yanyu Li, Mengshu Sun, Weiwen Jiang, Runbin Shi, Xue Lin, Yanzhi Wang:
MSP: An FPGA-Specific Mixed-Scheme, Multi-Precision Deep Neural Network Quantization Framework. CoRR abs/2009.07460 (2020) - [i2]Sung-En Chang, Yanyu Li, Mengshu Sun, Runbin Shi, Hayden Kwok-Hay So, Xuehai Qian, Yanzhi Wang, Xue Lin:
Mix and Match: A Novel FPGA-Centric Deep Neural Network Quantization Framework. CoRR abs/2012.04240 (2020)
2010 – 2019
- 2018
- [c2]Ian En-Hsu Yen, Wei-Cheng Lee, Kai Zhong, Sung-En Chang, Pradeep Ravikumar, Shou-De Lin:
MixLasso: Generalized Mixed Regression via Convex Atomic-Norm Regularization. NeurIPS 2018: 10891-10899 - [i1]Sung-En Chang, Xun Zheng, Ian En-Hsu Yen, Pradeep Ravikumar, Rose Yu:
Learning Tensor Latent Features. CoRR abs/1810.04754 (2018) - 2017
- [c1]Ian En-Hsu Yen, Wei-Cheng Lee, Sung-En Chang, Arun Sai Suggala, Shou-De Lin, Pradeep Ravikumar:
Latent Feature Lasso. ICML 2017: 3949-3957
Coauthor Index
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