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Books and Theses
- 2014
[b1]Minghai Qin:
Constrained Codes and Signal Processing for Non-Volatile Memories. University of California, San Diego, USA, 2014
Journal Articles
- 2018
- [j8]Bing Fan, Minghai Qin
, Paul H. Siegel:
Enhancing the Expected Lifetime of NAND Flash by Short q-Ary WOM Codes. IEEE Commun. Lett. 22(7): 1302-1305 (2018) - 2017
- [j7]Minghai Qin, Jing Guo, Aman Bhatia, Albert Guillen i Fabregas, Paul H. Siegel:
Polar Code Constructions Based on LLR Evolution. IEEE Commun. Lett. 21(6): 1221-1224 (2017) - [j6]Minghai Qin, Robert Mateescu, Chao Sun, Zvonimir Bandic:
Low Read Latency Rewriting Codes for Multi-Level 3-D NAND Flash. IEEE Commun. Lett. 21(7): 1477-1480 (2017) - 2015
- [j5]Borja Peleato, Rajiv Agarwal, John M. Cioffi, Minghai Qin, Paul H. Siegel:
Adaptive Read Thresholds for NAND Flash. IEEE Trans. Commun. 63(9): 3069-3081 (2015) - 2014
- [j4]Minghai Qin, Eitan Yaakobi, Paul H. Siegel:
Constrained Codes that Mitigate Inter-Cell Interference in Read/Write Cycles for Flash Memories. IEEE J. Sel. Areas Commun. 32(5): 836-846 (2014) - [j3]Aman Bhatia, Minghai Qin, Aravind R. Iyengar, Brian M. Kurkoski, Paul H. Siegel:
Lattice-Based WOM Codes for Multilevel Flash Memories. IEEE J. Sel. Areas Commun. 32(5): 933-945 (2014) - [j2]Minghai Qin, Eitan Yaakobi, Paul H. Siegel:
Optimized Cell Programming for Flash Memories With Quantizers. IEEE Trans. Inf. Theory 60(5): 2780-2795 (2014) - 2013
- [j1]Minghai Qin, Eitan Yaakobi, Paul H. Siegel:
Time-Space Constrained Codes for Phase-Change Memories. IEEE Trans. Inf. Theory 59(8): 5102-5114 (2013)
Conference and Workshop Papers
- 2024
- [c43]Gen Li, Lu Yin, Jie Ji, Wei Niu, Minghai Qin, Bin Ren, Linke Guo, Shiwei Liu, Xiaolong Ma:
NeurRev: Train Better Sparse Neural Network Practically via Neuron Revitalization. ICLR 2024 - [c42]Jie Ji, Gen Li, Lu Yin, Minghai Qin, Geng Yuan, Linke Guo, Shiwei Liu, Xiaolong Ma:
Advancing Dynamic Sparse Training by Exploring Optimization Opportunities. ICML 2024 - [c41]Jiacheng Guo, Huiming Sun, Minghai Qin, Hongkai Yu, Tianyun Zhang:
A Min-Max Optimization Framework for Multi-task Deep Neural Network Compression. ISCAS 2024: 1-5 - [c40]Minghai Qin, Chao Sun, Jaco Hofmann, Dejan Vucinic:
DISCO: Distributed Inference with Sparse Communications. WACV 2024: 2421-2429 - 2023
- [c39]Yanyu Li, Changdi Yang, Pu Zhao, Geng Yuan, Wei Niu, Jiexiong Guan, Hao Tang, Minghai Qin, Qing Jin, Bin Ren, Xue Lin, Yanzhi Wang:
Towards Real-Time Segmentation on the Edge. AAAI 2023: 1468-1476 - [c38]Zhenglun Kong, Haoyu Ma, Geng Yuan, Mengshu Sun, Yanyue Xie, Peiyan Dong, Xin Meng, Xuan Shen, Hao Tang, Minghai Qin, Tianlong Chen, Xiaolong Ma, Xiaohui Xie, Zhangyang Wang, Yanzhi Wang:
Peeling the Onion: Hierarchical Reduction of Data Redundancy for Efficient Vision Transformer Training. AAAI 2023: 8360-8368 - [c37]Gen Li, Jie Ji, Minghai Qin, Wei Niu, Bin Ren, Fatemeh Afghah, Linke Guo, Xiaolong Ma:
Towards High-Quality and Efficient Video Super-Resolution via Spatial-Temporal Data Overfitting. CVPR 2023: 10259-10269 - [c36]Changdi Yang, Pu Zhao, Yanyu Li, Wei Niu, Jiexiong Guan, Hao Tang, Minghai Qin, Bin Ren, Xue Lin, Yanzhi Wang:
Pruning Parameterization with Bi-level Optimization for Efficient Semantic Segmentation on the Edge. CVPR 2023: 15402-15412 - [c35]Yifan Gong, Pu Zhao, Zheng Zhan, Yushu Wu, Chao Wu, Zhenglun Kong, Minghai Qin, Caiwen Ding, Yanzhi Wang:
Condense: A Framework for Device and Frequency Adaptive Neural Network Models on the Edge. DAC 2023: 1-6 - [c34]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 - [c33]Sizhe Chen, Geng Yuan, Xinwen Cheng, Yifan Gong, Minghai Qin, Yanzhi Wang, Xiaolin Huang:
Self-Ensemble Protection: Training Checkpoints Are Good Data Protectors. ICLR 2023 - [c32]Xuan Shen, Zhenglun Kong, Minghai Qin, Peiyan Dong, Geng Yuan, Xin Meng, Hao Tang, Xiaolong Ma, Yanzhi Wang:
Data Level Lottery Ticket Hypothesis for Vision Transformers. IJCAI 2023: 1378-1386 - 2022
- [c31]Zejiang Hou, Minghai Qin, Fei Sun, Xiaolong Ma, Kun Yuan, Yi Xu, Yen-Kuang Chen, Rong Jin, Yuan Xie, Sun-Yuan Kung:
CHEX: CHannel EXploration for CNN Model Compression. CVPR 2022: 12277-12288 - [c30]Guyue Huang, Haoran Li, Minghai Qin, Fei Sun, Yufei Ding, Yuan Xie:
Shfl-BW: accelerating deep neural network inference with tensor-core aware weight pruning. DAC 2022: 1153-1158 - [c29]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 - [c28]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 - [c27]Yushu Wu, Yifan Gong, Pu Zhao, Yanyu Li, Zheng Zhan, Wei Niu, Hao Tang, Minghai Qin, Bin Ren, Yanzhi Wang:
Compiler-Aware Neural Architecture Search for On-Mobile Real-time Super-Resolution. ECCV (19) 2022: 92-111 - [c26]Zhenglun Kong, Peiyan Dong, Xiaolong Ma, Xin Meng, Wei Niu, Mengshu Sun, Xuan Shen, Geng Yuan, Bin Ren, Hao Tang, Minghai Qin, Yanzhi Wang:
SPViT: Enabling Faster Vision Transformers via Latency-Aware Soft Token Pruning. ECCV (11) 2022: 620-640 - [c25]Yifan Gong, Zheng Zhan, Pu Zhao, Yushu Wu, Chao Wu, Caiwen Ding, Weiwen Jiang, Minghai Qin, Yanzhi Wang:
All-in-One: A Highly Representative DNN Pruning Framework for Edge Devices with Dynamic Power Management. ICCAD 2022: 133:1-133:9 - [c24]Xiaolong Ma, Minghai Qin, Fei Sun, Zejiang Hou, Kun Yuan, Yi Xu, Yanzhi Wang, Yen-Kuang Chen, Rong Jin, Yuan Xie:
Effective Model Sparsification by Scheduled Grow-and-Prune Methods. ICLR 2022 - [c23]Minghai Qin, Tianyun Zhang, Fei Sun, Yen-Kuang Chen, Makan Fardad, Yanzhi Wang, Yuan Xie:
Compact Multi-level Sparse Neural Networks with Input Independent Dynamic Rerouting. ICTAI 2022: 555-562 - [c22]Zhenzhen Wang, Minghai Qin, Yen-Kuang Chen:
Learning from the CNN-based Compressed Domain. WACV 2022: 4000-4008 - 2021
- [c21]Xiaolong Ma, Geng Yuan, Xuan Shen, Tianlong Chen, Xuxi Chen, Xiaohan Chen, Ning Liu, Minghai Qin, Sijia Liu, Zhangyang Wang, Yanzhi Wang:
Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot? NeurIPS 2021: 12749-12760 - [c20]Geng Yuan, Xiaolong Ma, Wei Niu, Zhengang Li, Zhenglun Kong, Ning Liu, Yifan Gong, Zheng Zhan, Chaoyang He, Qing Jin, Siyue Wang, Minghai Qin, Bin Ren, Yanzhi Wang, Sijia Liu, Xue Lin:
MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge. NeurIPS 2021: 20838-20850 - 2020
- [c19]Kai Xu, Minghai Qin, Fei Sun, Yuhao Wang, Yen-Kuang Chen, Fengbo Ren:
Learning in the Frequency Domain. CVPR 2020: 1737-1746 - [c18]Fei Sun, Minghai Qin, Tianyun Zhang, Liu Liu, Yen-Kuang Chen, Yuan Xie:
INVITED: Computation on Sparse Neural Networks and its Implications for Future Hardware. DAC 2020: 1-6 - 2019
- [c17]Minghai Qin, Robert Mateescu, Qingbo Wang, Cyril Guyot, Dejan Vucinic, Zvonimir Bandic:
Garbage Collection Algorithms for Meta Data Updates in NAND Flash. ICC 2019: 1-5 - [c16]Wen Ma, Pi-Feng Chiu, Won Ho Choi, Minghai Qin, Daniel Bedau, Martin Lueker-Boden:
Non-Volatile Memory Array Based Quantization- and Noise-Resilient LSTM Neural Networks. ICRC 2019: 25-33 - [c15]Pi-Feng Chiu, Won Ho Choi, Wen Ma, Minghai Qin, Martin Lueker-Boden:
A Binarized Neural Network Accelerator with Differential Crosspoint Memristor Array for Energy-Efficient MAC Operations. ISCAS 2019: 1-5 - 2018
- [c14]Minghai Qin, Dejan Vucinic:
Training Recurrent Neural Networks against Noisy Computations during Inference. ACSSC 2018: 71-75 - [c13]Wen Ma, Minghai Qin, Won Ho Choi, Pi-Feng Chiu, Martin Lueker-Boden:
Improving Noise Tolerance of Hardware Accelerated Artificial Neural Networks. ICMLA 2018: 797-801 - [c12]Minghai Qin:
Hamming-Distance-Based Binary Representation of Numbers. ISIT 2018: 2202-2205 - [c11]Minghai Qin:
Hamming-Distance-Based Binary Representation of Numbers. ITA 2018: 1-9 - 2017
- [c10]Minghai Qin:
Fractional Bits-Per-Cell for NAND Flash with Low Read Latency. GLOBECOM 2017: 1-6 - 2016
- [c9]Ying Wang, Minghai Qin, Krishna R. Narayanan, Anxiao Jiang, Zvonimir Bandic:
Joint Source-Channel Decoding of Polar Codes for Language-Based Sources. GLOBECOM 2016: 1-6 - 2015
- [c8]Jing Guo, Jossy Sayir, Minghai Qin, Albert Guillen i Fabregas:
An alternative proof of channel polarization for channels with arbitrary input alphabets. Allerton 2015: 522-529 - [c7]Minghai Qin, Robert Mateescu, Cyril Guyot, Zvonimir Bandic:
Balanced codes for data retention of multi-level flash memories with fast page read. Allerton 2015: 704-711 - 2014
- [c6]Jing Guo, Minghai Qin, Albert Guillen i Fabregas, Paul H. Siegel:
Enhanced belief propagation decoding of polar codes through concatenation. ISIT 2014: 2987-2991 - 2013
- [c5]Minghai Qin, Anxiao Andrew Jiang, Paul H. Siegel:
Parallel programming of rank modulation. ISIT 2013: 719-723 - 2012
- [c4]Borja Peleato, Rajiv Agarwal, John M. Cioffi, Minghai Qin, Paul H. Siegel:
Towards minimizing read time for NAND flash. GLOBECOM 2012: 3219-3224 - [c3]Minghai Qin, Eitan Yaakobi, Paul H. Siegel:
Optimized cell programming for flash memories with quantizers. ISIT 2012: 995-999 - [c2]Lele Wang, Minghai Qin, Eitan Yaakobi, Young-Han Kim, Paul H. Siegel:
WOM with retained messages. ISIT 2012: 1396-1400 - 2011
- [c1]Minghai Qin, Eitan Yaakobi, Paul H. Siegel:
Time-Space Constrained Codes for Phase-Change Memories. GLOBECOM 2011: 1-6
Informal and Other Publications
- 2024
- [i27]Gen Li, Zhihao Shu, Jie Ji, Minghai Qin, Fatemeh Afghah, Wei Niu, Xiaolong Ma:
Data Overfitting for On-Device Super-Resolution with Dynamic Algorithm and Compiler Co-Design. CoRR abs/2407.02813 (2024) - [i26]Minghai Qin:
The Uniqueness of LLaMA3-70B with Per-Channel Quantization: An Empirical Study. CoRR abs/2408.15301 (2024) - 2023
- [i25]Minghai Qin, Chao Sun, Jaco Hofmann, Dejan Vucinic:
DISCO: Distributed Inference with Sparse Communications. CoRR abs/2302.11180 (2023) - [i24]Gen Li, Jie Ji, Minghai Qin, Wei Niu, Bin Ren, Fatemeh Afghah, Linke Guo, Xiaolong Ma:
Towards High-Quality and Efficient Video Super-Resolution via Spatial-Temporal Data Overfitting. CoRR abs/2303.08331 (2023) - 2022
- [i23]Borja Peleato, Rajiv Agarwal, John M. Cioffi, Minghai Qin, Paul H. Siegel:
Adaptive Read Thresholds for NAND Flash. CoRR abs/2202.05661 (2022) - [i22]Guyue Huang, Haoran Li, Minghai Qin, Fei Sun, Yufei Ding, Yuan Xie:
Shfl-BW: Accelerating Deep Neural Network Inference with Tensor-Core Aware Weight Pruning. CoRR abs/2203.05016 (2022) - [i21]Zejiang Hou, Minghai Qin, Fei Sun, Xiaolong Ma, Kun Yuan, Yi Xu, Yen-Kuang Chen, Rong Jin, Yuan Xie, Sun-Yuan Kung:
CHEX: CHannel EXploration for CNN Model Compression. CoRR abs/2203.15794 (2022) - [i20]Yushu Wu, Yifan Gong, Pu Zhao, Yanyu Li, Zheng Zhan, Wei Niu, Hao Tang, Minghai Qin, Bin Ren, Yanzhi Wang:
Compiler-Aware Neural Architecture Search for On-Mobile Real-time Super-Resolution. CoRR abs/2207.12577 (2022) - [i19]Xuan Shen, Zhenglun Kong, Minghai Qin, Peiyan Dong, Geng Yuan, Xin Meng, Hao Tang, Xiaolong Ma, Yanzhi Wang:
The Lottery Ticket Hypothesis for Vision Transformers. CoRR abs/2211.01484 (2022) - [i18]Zhenglun Kong, Haoyu Ma, Geng Yuan, Mengshu Sun, Yanyue Xie, Peiyan Dong, Xin Meng, Xuan Shen, Hao Tang, Minghai Qin, Tianlong Chen, Xiaolong Ma, Xiaohui Xie, Zhangyang Wang, Yanzhi Wang:
Peeling the Onion: Hierarchical Reduction of Data Redundancy for Efficient Vision Transformer Training. CoRR abs/2211.10801 (2022) - [i17]Sizhe Chen, Geng Yuan, Xinwen Cheng, Yifan Gong, Minghai Qin, Yanzhi Wang, Xiaolin Huang:
Self-Ensemble Protection: Training Checkpoints Are Good Data Protectors. CoRR abs/2211.12005 (2022) - [i16]Yifan Gong, Zheng Zhan, Pu Zhao, Yushu Wu, Chao Wu, Caiwen Ding, Weiwen Jiang, Minghai Qin, Yanzhi Wang:
All-in-One: A Highly Representative DNN Pruning Framework for Edge Devices with Dynamic Power Management. CoRR abs/2212.05122 (2022) - 2021
- [i15]Xiaolong Ma, Minghai Qin, Fei Sun, Zejiang Hou, Kun Yuan, Yi Xu, Yanzhi Wang, Yen-Kuang Chen, Rong Jin, Yuan Xie:
Effective Model Sparsification by Scheduled Grow-and-Prune Methods. CoRR abs/2106.09857 (2021) - [i14]Xiaolong Ma, Geng Yuan, Xuan Shen, Tianlong Chen, Xuxi Chen, Xiaohan Chen, Ning Liu, Minghai Qin, Sijia Liu, Zhangyang Wang, Yanzhi Wang:
Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot? CoRR abs/2107.00166 (2021) - [i13]Geng Yuan, Xiaolong Ma, Wei Niu, Zhengang Li, Zhenglun Kong, Ning Liu, Yifan Gong, Zheng Zhan, Chaoyang He, Qing Jin, Siyue Wang, Minghai Qin, Bin Ren, Yanzhi Wang, Sijia Liu, Xue Lin:
MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge. CoRR abs/2110.14032 (2021) - [i12]Fei Sun, Minghai Qin, Tianyun Zhang, Xiaolong Ma, Haoran Li, Junwen Luo, Zihao Zhao, Yen-Kuang Chen, Yuan Xie:
Load-balanced Gather-scatter Patterns for Sparse Deep Neural Networks. CoRR abs/2112.10898 (2021) - [i11]Minghai Qin, Tianyun Zhang, Fei Sun, Yen-Kuang Chen, Makan Fardad, Yanzhi Wang, Yuan Xie:
Compact Multi-level Sparse Neural Networks with Input Independent Dynamic Rerouting. CoRR abs/2112.10930 (2021) - [i10]Zhenglun Kong, Peiyan Dong, Xiaolong Ma, Xin Meng, Wei Niu, Mengshu Sun, Bin Ren, Minghai Qin, Hao Tang, Yanzhi Wang:
SPViT: Enabling Faster Vision Transformers via Soft Token Pruning. CoRR abs/2112.13890 (2021) - 2020
- [i9]Wen Ma, Pi-Feng Chiu, Won Ho Choi, Minghai Qin, Daniel Bedau, Martin Lueker-Boden:
Non-Volatile Memory Array Based Quantization- and Noise-Resilient LSTM Neural Networks. CoRR abs/2002.10636 (2020) - [i8]Kai Xu, Minghai Qin, Fei Sun, Yuhao Wang, Yen-Kuang Chen, Fengbo Ren:
Learning in the Frequency Domain. CoRR abs/2002.12416 (2020) - [i7]Tianyun Zhang, Xiaolong Ma, Zheng Zhan, Shanglin Zhou, Minghai Qin, Fei Sun, Yen-Kuang Chen, Caiwen Ding, Makan Fardad, Yanzhi Wang:
A Unified DNN Weight Compression Framework Using Reweighted Optimization Methods. CoRR abs/2004.05531 (2020) - [i6]Fei Sun, Minghai Qin, Tianyun Zhang, Liu Liu, Yen-Kuang Chen, Yuan Xie:
Computation on Sparse Neural Networks: an Inspiration for Future Hardware. CoRR abs/2004.11946 (2020) - 2018
- [i5]Minghai Qin, Dejan Vucinic:
Training Recurrent Neural Networks against Noisy Computations during Inference. CoRR abs/1807.06555 (2018) - [i4]Minghai Qin, Dejan Vucinic:
Noisy Computations during Inference: Harmful or Helpful? CoRR abs/1811.10649 (2018) - 2017
- [i3]Minghai Qin, Chao Sun, Dejan Vucinic:
Robustness of Neural Networks against Storage Media Errors. CoRR abs/1709.06173 (2017) - 2016
- [i2]Ying Wang, Minghai Qin, Krishna R. Narayanan, Anxiao Jiang, Zvonimir Bandic:
Joint Source-Channel Decoding of Polar Codes for Language-Based Source. CoRR abs/1601.06184 (2016) - 2012
- [i1]Minghai Qin, Eitan Yaakobi, Paul H. Siegel:
Time-Space Constrained Codes for Phase-Change Memories. CoRR abs/1207.4530 (2012)
Coauthor Index
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