ADMM-based Weight Pruning for Real-Time Deep Learning Acceleration on Mobile Devices

Author:

Li Hongjia1,Liu Ning1,Ma Xiaolong1,Lin Sheng1,Ye Shaokai2,Zhang Tianyun2,Lin Xue1,Xu Wenyao3,Wang Yanzhi1

Affiliation:

1. Northeastern University, Boston, MA, USA

2. Syracuse University, Syracuse, NY, USA

3. University at Buffalo, Buffalo, NY, USA

Funder

National Science Foundation Awards

Publisher

ACM

Reference27 articles.

1. Stephen Boyd Neal Parikh Eric Chu Borja Peleato Jonathan Eckstein etal 2011. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends® in Machine learning 3 1 (2011) 1--122. 10.1561/2200000016 Stephen Boyd Neal Parikh Eric Chu Borja Peleato Jonathan Eckstein et al. 2011. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends® in Machine learning 3 1 (2011) 1--122. 10.1561/2200000016

2. G. Bradski. 2000. The OpenCV Library. Dr. Dobb's Journal of Software Tools (2000). G. Bradski. 2000. The OpenCV Library. Dr. Dobb's Journal of Software Tools (2000).

3. Sharan Chetlur Cliff Woolley Philippe Vandermersch Jonathan Cohen John Tran Bryan Catanzaro and Evan Shelhamer. 2014. cudnn: Efficient primitives for deep learning. arXiv preprint arXiv:1410.0759 (2014). Sharan Chetlur Cliff Woolley Philippe Vandermersch Jonathan Cohen John Tran Bryan Catanzaro and Evan Shelhamer. 2014. cudnn: Efficient primitives for deep learning. arXiv preprint arXiv:1410.0759 (2014).

4. Xiaoliang Dai Hongxu Yin and Niraj K. Jha. 2017. NeST: a neural network synthesis tool based on a grow-and-prune paradigm. arXiv preprint arXiv:1711.02017 (2017). Xiaoliang Dai Hongxu Yin and Niraj K. Jha. 2017. NeST: a neural network synthesis tool based on a grow-and-prune paradigm. arXiv preprint arXiv:1711.02017 (2017).

5. Gene H. Golub and Charles F. Van Loan. 2012. Matrix computations. Vol. 3. JHU press. Gene H. Golub and Charles F. Van Loan. 2012. Matrix computations. Vol. 3. JHU press.

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