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Sung-En Chang

Observed names: Chang, Sung-En, Chang, Sung-en
ORCID: 0000-0001-8585-503X
Observed ORCID: 0000-0001-8585-503X
OpenAlex ID: A5079385080
Mix and Match: A Novel FPGA-Centric Deep Neural Network Quantization Framework
2021 · Sung-En Chang, Yanyu Li, et al. · IEEE International Symposium on High-Performance Computer Architecture (HPCA) · 136
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FILM-QNN: Efficient FPGA Acceleration of Deep Neural Networks with Intra-Layer, Mixed-Precision Quantization
2022 · Mengshu Sun, Zhengang Li, et al. · ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA) · 110
SDA: Low-Bit Stable Diffusion Acceleration on Edge FPGAs
2024 · Geng Yang, Yanyue Xie, et al. · International Conference on Field Programmable Logic and Applications (FPL) · 19
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Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm
2022 · Shaoyi Huang, Dongkuan Xu, et al. · Proceedings of the conference - Association for Computational Linguistics. Meeting · 18
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Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm
2021 · Huang, Shaoyi, Xu, Dongkuan, et al. · arXiv (Cornell University) · 18
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MSP: An FPGA-Specific Mixed-Scheme, Multi-Precision Deep Neural Network Quantization Framework
2020 · Chang, Sung-En, Li, Yanyu, et al. · arXiv (Cornell University) · 12
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7B Fully Open Source Moxin-LLM/VLM -- From Pretraining to GRPO-based Reinforcement Learning Enhancement
2024 · Zhao, Pu, Shen, Xuan, et al. · arXiv (Cornell University) · 11
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You Already Have It: A Generator-Free Low-Precision DNN Training Framework Using Stochastic Rounding
2022 · Geng Yuan, Sung-En Chang, et al. · Lecture notes in computer science · 9
RMSMP: A Novel Deep Neural Network Quantization Framework with Row-wise Mixed Schemes and Multiple Precisions
2021 · Sung-En Chang, Yanyu Li, et al. · IEEE/CVF International Conference on Computer Vision (ICCV) · 8
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Open-Source Multimodal Moxin Models with Moxin-VLM and Moxin-VLA
2025 · Zhao, Pu, Akbari, Arash, et al. · arXiv (Cornell University) · 7
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ESRU: Extremely Low-Bit and Hardware-Efficient Stochastic Rounding Unit Design for Low-Bit DNN Training
2023 · Sung-En Chang, Geng Yuan, et al. · Design, Automation, and Test in Europe · 7
MRMAD: A Multi-Round Multi-Audio Benchmark for Evaluating Acoustic Degradation Perception in Large Audio-Language Models
2026 · Yize Li, Ningyuan Yang, et al. · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · 4
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Latent Feature Lasso.
2017 · Ian E H Yen, Wei-Cheng Lee, et al. · PubMed · 4
MixLasso: Generalized Mixed Regression via Convex Atomic-Norm Regularization
2018 · Ian En-Hsu Yen, Wei-Cheng Lee, et al. · neural information processing systems · 2
SuperFlow: A Fully-Customized RTL-to-GDS Design Automation Flow for Adiabatic Quantum- Flux - Parametron Superconducting Circuits
2024 · Yanyue Xie, Peiyan Dong, et al. · Design, Automation, and Test in Europe · 1
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ILMPQ : An Intra-Layer Multi-Precision Deep Neural Network Quantization framework for FPGA
2021 · Chang, Sung-En, Li, Yanyu, et al. · arXiv (Cornell University) · 1
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Learning Tensor Latent Features.
2018 · Sung-En Chang, Xun Zheng, et al. · arXiv (Cornell University) · 1
Efficient Tensor Decomposition with Boolean Factors
2018 · Chang, Sung-En, Zheng, Xun, et al. · arXiv (Cornell University) · 1
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A novel model quantization scheme for efficient deep neural networks inference and training
2024 · Sung-En Chang · Digital Repository Service (Northeastern University)
Hardware-efficient stochastic rounding unit design for DNN training: late breaking results
2022 · Sung-En Chang, Geng Yuan, et al. · ACM/IEEE Design Automation Conference (DAC)
Works count: 20
Citation count: 369
H-index: 8
I10-index: 7
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