Latent Feature Lasso. - Ian En-Hsu Yen; Wei-Cheng Lee; Sung-En Chang; Arun Sai Suggala; Shou-De Lin; Pradeep Ravikumar (ICML, 2017)

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 (NeurIPS, 2018)

Learning Tensor Latent Features. - Sung-En Chang; Xun Zheng; Ian En-Hsu Yen; Pradeep Ravikumar; Rose Yu (CoRR, 2018)

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 001; Yanzhi Wang (CoRR, 2020)

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 001 (HPCA, 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 001; Yanzhi Wang; Xue Lin 001 (ICCV, 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 004; Mimi Xie; Hang Liu 001; Caiwen Ding (CoRR, 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 001 (CoRR, 2021)

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 004; Mimi Xie; Sanguthevar Rajasekaran; Hang Liu 001; Caiwen Ding (ACL (1), 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 001; Zhenman Fang; Yanzhi Wang (DAC, 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; et al. (ECCV (12), 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 001; Zhenman Fang (FPGA, 2022)

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 001; Zhenman Fang; Yanzhi Wang (DATE, 2023)

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 006; Sung-En Chang; Xufeng Zhang; Xue Lin 001; Caiwen Ding; Nobuyuki Yoshikawa; Olivia Chen; et al. (DATE, 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 001; Weiying Xie; Yanzhi Wang; Xue Lin 001; Zhenman Fang (FPL, 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 (IJCAI, 2024)

Fully Open Source Moxin-7B Technical Report. - Pu Zhao 001; Xuan Shen; Zhenglun Kong; Yixin Shen; Sung-En Chang; Timothy Rupprecht; Lei Lu; Enfu Nan; Changdi Yang; Yumei He; Xingchen Xu; Yu Huang; et al. (CoRR, 2024)

Perturbation-efficient Zeroth-order Optimization for Hardware-friendly On-device Training. - Qitao Tan; Sung-En Chang; Rui Xia; Huidong Ji; Chence Yang; Ci Zhang; Jun Liu; Zheng Zhan 004; Zhou Zou; Yanzhi Wang; Jin Lu; Geng Yuan (CoRR, 2025)