Fetching the paper…
Reading the bibliography…
Accelerating DNN execution on various resource-limited computing platforms has been a long-standing problem.
Model selection and estimation in regression with grouped variables
Ming Yuan and Yi Lin · 2006
Earlier work this paper cites.
Enhancing sparsity by reweighted l1 minimization
Emmanuel J Candes, Michael B Wakin, and Stephen P Boyd · 2008
Earlier work this paper cites.
Distributed optimization and statistical learning via the alternating direction method of multipliers
Stephen Boyd, Neal Parikh, Eric Chu, Borja Peleato, and Jonathan Eckstein · 2011
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
Earlier work this paper cites.
A convolutional encoder model for neural machine translation
Jonas Gehring, Michael Auli, David Grangier, and Yann N Dauphin · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Network trimming: A data-driven neuron pruning approach towards efficient deep architectures
Hengyuan Hu, Rui Peng, Yu-Wing Tai, and Chi-Keung Tang · 2016
Earlier work this paper cites.
Learning structured sparsity in deep neural networks
Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li · 2016
Earlier work this paper cites.
Ese: Efficient speech recognition engine with sparse lstm on fpga
Song Han, Junlong Kang, Huizi Mao, Yiming Hu, Xin Li, Yubin Li, Dongliang Xie, Hong Luo, Song Yao, Yu Wang, Huazhong Yang, and William J. Dally · 2017
Earlier work this paper cites.
Channel pruning for accelerating very deep neural networks
Yihui He, Xiangyu Zhang, and Jian Sun · 2017
Cited alongside, same era.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
Cited alongside, same era.
Learning efficient convolutional networks through network slimming
Zhuang Liu, Jianguo Li, Zhiqiang Shen, Gao Huang, Shoumeng Yan, and Changshui Zhang · 2017
Cited alongside, same era.
Towards self-driving car using convolutional neural network and road lane detector
Brilian Tafjira Nugraha, Shun-Feng Su, et al · 2017
Cited alongside, same era.
TVM: An automated end-to-end optimizing compiler for deep learning
Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Haichen Shen, Meghan Cowan, Leyuan Wang, Yuwei Hu, Luis Ceze, et al · 2018
Cited alongside, same era.
Adam-admm: A unified, systematic framework of structured weight pruning for dnns
Tianyun Zhang, Kaiqi Zhang, Shaokai Ye, Jian Tang, Wujie Wen, Xue Lin, Makan Fardad, and Yanzhi Wang · 2018
Later among the works it cites.
Improving deep neural network sparsity through decorrelation regularization
Xiaotian Zhu, Wengang Zhou, and Houqiang Li · 2018
Later among the works it cites.
Discrimination-aware channel pruning for deep neural networks
Zhuangwei Zhuang, Mingkui Tan, Bohan Zhuang, Jing Liu, Yong Guo, Qingyao Wu, Junzhou Huang, and Jinhui Zhu · 2018
Later among the works it cites.
E-rnn: design optimization for efficient recurrent neural networks in fpgas
Zhe Li, Caiwen Ding, Shuo Wang, Wujie Wen, Youwei Zhuo, Xue Lin, Xuehai Qian, and Yanzhi Wang · 2019
Later among the works it cites.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Amc: Automl for model compression and acceleration on mobile devices
Yihui He, Ji Lin, Zhijian Liu, Hanrui Wang, Li-Jia Li, and Song Han · 2018
Cited alongside, same era.
2pfpce: Two-phase filter pruning based on conditional entropy
Chuhan Min, Aosen Wang, Yiran Chen, Wenyao Xu, and Xin Chen · 2018
Cited alongside, same era.
C-lstm: Enabling efficient lstm using structured compression techniques on fpgas
Shuo Wang, Zhe Li, Caiwen Ding, Bo Yuan, Qinru Qiu, Yanzhi Wang, and Yun Liang · 2018
Cited alongside, same era.
A systematic dnn weight pruning framework using alternating direction method of multipliers
Tianyun Zhang, Shaokai Ye, Kaiqi Zhang, Jian Tang, Wujie Wen, Makan Fardad, and Yanzhi Wang · 2018
Cited alongside, same era.
https://github.com/alibaba/MNN
Cited in the paper.
https://www.tensorflow.org/mobile/tflite/
Cited in the paper.
Admm-nn: an algorithm-hardware co-design framework of dnns using alternating direction methods of multipliers
Ao Ren, Tianyun Zhang, Shaokai Ye, Wenyao Xu, Xuehai Qian, Xue Lin, and Yanzhi Wang · 2019
Later among the works it cites.
Autoslim: An automatic dnn structured pruning framework for ultra-high compression rates
Ning Liu, Xiaolong Ma, Zhiyuan Xu, Yanzhi Wang, Jian Tang, and Jieping Ye · 2020
Closest in time.
Tiny but accurate: A pruned, quantized and optimized memristor crossbar framework for ultra efficient dnn implementation
Xiaolong Ma, Geng Yuan, Sheng Lin, Caiwen Ding, Fuxun Yu, Tao Liu, Wujie Wen, Xiang Chen, and Yanzhi Wang · 2020
Closest in time.