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Progressive DNN Compression: A Key to Achieve Ultra-High Weight Pruning and Quantization Rates using ADMM.

S. Ye, X. Feng, T. Zhang, X. Ma, S. Lin, Z. Li, K. Xu, W. Wen, S. Liu, J. Tang, M. Fardad, X. Lin, Y. Liu, and Y. Wang. CoRR, (2019)

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Shuai Zhang University of Stuttgart

Guanhua Zhang University of Stuttgart

Dr. -Ing. Jing Zhang University of Stuttgart

Jixing Zhang University of Stuttgart

Dr. Xi Zhang University of Stuttgart

Other publications of authors with the same name

A method to build reconfigurable architectures by extracting common subgraphs.T. Zhang, R. Zhang, L. Wang, and Y. Hu. ASICON, page 970-973. IEEE, (2011)Progressive Weight Pruning of Deep Neural Networks using ADMM.S. Ye, T. Zhang, K. Zhang, J. Li, K. Xu, Y. Yang, F. Yu, J. Tang, M. Fardad, S. Liu and 3 other author(s). CoRR, (2018)ADMM-NN: An Algorithm-Hardware Co-Design Framework of DNNs Using Alternating Direction Methods of Multipliers.A. Ren, T. Zhang, S. Ye, J. Li, W. Xu, X. Qian, X. Lin, and Y. Wang. ASPLOS, page 925-938. ACM, (2019)Beyond Adversarial Training: Min-Max Optimization in Adversarial Attack and Defense.J. Wang, T. Zhang, S. Liu, P. Chen, J. Xu, M. Fardad, and B. Li. CoRR, (2019)Reinforced Adversarial Attacks on Deep Neural Networks Using ADMM.P. Zhao, K. Xu, T. Zhang, M. Fardad, Y. Wang, and X. Lin. GlobalSIP, page 1169-1173. IEEE, (2018)ADMM-based Weight Pruning for Real-Time Deep Learning Acceleration on Mobile Devices.H. Li, N. Liu, X. Ma, S. Lin, S. Ye, T. Zhang, X. Lin, W. Xu, and Y. Wang. ACM Great Lakes Symposium on VLSI, page 501-506. ACM, (2019)A Systematic DNN Weight Pruning Framework Using Alternating Direction Method of Multipliers.T. Zhang, S. Ye, K. Zhang, J. Tang, W. Wen, M. Fardad, and Y. Wang. ECCV (8), volume 11212 of Lecture Notes in Computer Science, page 191-207. Springer, (2018)Data Temperature-Aware Bloom Filters for Flash-Based Storage.W. Li, T. Zhang, D. Zhang, Y. Hu, K. Xie, and T. Zhu. HPCC/SmartCity/DSS, page 1421-1428. IEEE, (2019)ADMM-NN: An Algorithm-Hardware Co-Design Framework of DNNs Using Alternating Direction Method of Multipliers.A. Ren, T. Zhang, S. Ye, J. Li, W. Xu, X. Qian, X. Lin, and Y. Wang. CoRR, (2018)A Unified Framework of DNN Weight Pruning and Weight Clustering/Quantization Using ADMM.S. Ye, T. Zhang, K. Zhang, J. Li, J. Xie, Y. Liang, S. Liu, X. Lin, and Y. Wang. CoRR, (2018)

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Disambiguation of "Zhang, Tianyun"

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Progressive DNN Compression: A Key to Achieve Ultra-High Weight Pruning and Quantization Rates using ADMM.

Please choose a person to relate this publication to

Shuai Zhang University of Stuttgart

Guanhua Zhang University of Stuttgart

Dr. -Ing. Jing Zhang University of Stuttgart

Jixing Zhang University of Stuttgart

Dr. Xi Zhang University of Stuttgart

Other publications of authors with the same name

Disambiguation

PUMA

Disambiguation of "Zhang, Tianyun"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Progressive DNN Compression: A Key to Achieve Ultra-High Weight Pruning and Quantization Rates using ADMM.

Please choose a person to relate this publication to

Shuai Zhang University of Stuttgart

Guanhua Zhang University of Stuttgart

Dr. -Ing. Jing Zhang University of Stuttgart

Jixing Zhang University of Stuttgart

Dr. Xi Zhang University of Stuttgart

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
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Progressive DNN Compression: A Key to Achieve Ultra-High Weight Pruning and Quantization Rates using ADMM.