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Weight pruning is a technique to make Deep Neural Network (DNN) inference more computationally efficient by reducing the number of model parameters over the course of training.
Why systolic architectures?
H. T. Kung · 1982
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Optimal brain damage
Yann LeCun, John S Denker, and Sara A Solla · 1990
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E Hinton · 2010
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cudnn: Efficient primitives for deep learning
Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Jonathan Cohen, John Tran, Bryan Catanzaro, and Evan Shelhamer · 2014
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The cifar-10 dataset, 2014
Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton · 2014
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Song Han, Huizi Mao, and William J Dally · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Pruning filters for efficient convnets
Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf · 2016
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Fixed point quantization of deep convolutional networks
Darryl Lin, Sachin Talathi, and Sreekanth Annapureddy · 2016
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Pruning convolutional neural networks for resource efficient inference
Pavlo Molchanov, Stephen Tyree, Tero Karras, Timo Aila, and Jan Kautz · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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Learning structured sparsity in deep neural networks
Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li · 2016
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Dawnbench: An end-to-end deep learning benchmark and competition
Cody Coleman, Deepak Narayanan, Daniel Kang, Tian Zhao, Jian Zhang, Luigi Nardi, Peter Bailis, Kunle Olukotun, Chris Ré, and Matei Zaharia · 2017
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Channel pruning for accelerating very deep neural networks
Yihui He, Xiangyu Zhang, and Jian Sun · 2017
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Cyclical learning rates for training neural networks
Leslie N Smith · 2017
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Eager pruning: algorithm and architecture support for fast training of deep neural networks
Jiaqi Zhang, Xiangru Chen, Mingcong Song, and Tao Li · 2019
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An energy-efficient deep convolutional neural network training accelerator for in situ personalization on smart devices
Seungkyu Choi, Jaehyeong Sim, Myeonggu Kang, Yeongjae Choi, Hyeonuk Kim, and Lee-Sup Kim · 2020
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Autopruner: An end-to-end trainable filter pruning method for efficient deep model inference
Jian-Hao Luo and Jianxin Wu · 2020
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Tensordash: Exploiting sparsity to accelerate deep neural network training
Mostafa Mahmoud, Isak Edo, Ali Hadi Zadeh, Omar Mohamed Awad, Gennady Pekhimenko, Jorge Albericio, and Andreas Moshovos · 2020
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https://images.nvidia.com/aem-dam/en-zz/Solutions/data-center/nvidia-ampere-architecture-whitepaper.pdf
Nvidia a100 tensor core architecture, 2020 · 2020
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Jonathan Frankle and Michael Carbin · 2018
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Rethinking the value of network pruning
Zhuang Liu, Mingjie Sun, Tinghui Zhou, Gao Huang, and Trevor Darrell · 2018
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Mixed precision training
Paulius Micikevicius, Sharan Narang, Jonah Alben, Gregory Diamos, Erich Elsen, David Garcia, Boris Ginsburg, Michael Houston, Oleksii Kuchaiev, Ganesh Venkatesh, and Hao Wu · 2018
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Rethinking the smaller-norm-less-informative assumption in channel pruning of convolution layers
Jianbo Ye, Xin Lu, Zhe Lin, and James Z Wang · 2018
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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
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Filter pruning via geometric median for deep convolutional neural networks acceleration
Yang He, Ping Liu, Ziwei Wang, Zhilan Hu, and Yi Yang · 2019
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Packing sparse convolutional neural networks for efficient systolic array implementations: Column combining under joint optimization
H. T. Kung, Bradley McDanel, and Sai Qian Zhang · 2019
Cited alongside, same era.
Designing network design spaces
Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, and Piotr Dollár · 2020
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Movement pruning: Adaptive sparsity by fine-tuning
Victor Sanh, Thomas Wolf, and Alexander M Rush · 2020
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Procrustes: a dataflow and accelerator for sparse deep neural network training
Dingqing Yang, Amin Ghasemazar, Xiaowei Ren, Maximilian Golub, Guy Lemieux, and Mieszko Lis · 2020
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Accelerating cnn training by pruning activation gradients
Xucheng Ye, Pengcheng Dai, Junyu Luo, Xin Guo, Yingjie Qi, Jianlei Yang, and Yiran Chen · 2020
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Accelerated sparse neural training: A provable and efficient method to find n: m transposable masks
Itay Hubara, Brian Chmiel, Moshe Island, Ron Banner, Seffi Naor, and Daniel Soudry · 2021
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Learning n:m fine-grained structured sparse neural networks from scratch
Aojun Zhou, Yukun Ma, Junnan Zhu, Jianbo Liu, Zhijie Zhang, Kun Yuan, Wenxiu Sun, and Hongsheng Li · 2021
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