Fetching the paper…
Reading the bibliography…
Recent breakthroughs in deep neural networks (DNNs) have fueled a tremendous demand for intelligent edge devices featuring on-site learning, while the practical realization of such systems remains a challenge due to the limited resources available at the edge and the required massive training costs for state-of-the-art (SOTA) DNNs.
Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
Earlier work this paper cites.
ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
1-bit stochastic gradient descent and its application to data-parallel distributed training of speech dnns
Frank Seide, Hao Fu, Jasha Droppo, Gang Li, and Dong Yu · 2014
Earlier work this paper cites.
Deep learning with limited numerical precision
Suyog Gupta, Ankur Agrawal, Kailash Gopalakrishnan, and Pritish Narayanan · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Earlier work this paper cites.
Residual networks behave like ensembles of relatively shallow networks, 2016
Andreas Veit, Michael Wilber, and Serge Belongie · 2016
Earlier work this paper cites.
Highway and residual networks learn unrolled iterative estimation
Klaus Greff, Rupesh K Srivastava, and Jürgen Schmidhuber · 2016
Earlier work this paper cites.
Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients
Shuchang Zhou, Yuxin Wu, Zekun Ni, Xinyu Zhou, He Wen, and Yuheng Zou · 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.
Pointer sentinel mixture models
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher · 2016
Earlier work this paper cites.
Accurate, large minibatch SGD: training imagenet in 1 hour
Priya Goyal, Piotr Dollár, Ross B. Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He · 2017
Earlier work this paper cites.
Extremely large minibatch sgd: Training resnet-50 on imagenet in 15 minutes
Takuya Akiba, Shuji Suzuki, and Keisuke Fukuda · 2017
Earlier work this paper cites.
Paulius Micikevicius, Sharan Narang, Jonah Alben, Gregory F. Diamos, Erich Elsen, David García, Boris Ginsburg, Michael Houston, Oleksii Kuchaiev, Ganesh Venkatesh, and Hao Wu · 2017
Earlier work this paper cites.
Understanding and optimizing asynchronous low-precision stochastic gradient descent
Christopher De Sa, Matthew Feldman, Christopher Ré, and Kunle Olukotun · 2017
Earlier work this paper cites.
Terngrad: Ternary gradients to reduce communication in distributed deep learning
Wei Wen, Cong Xu, Feng Yan, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li · 2017
Earlier work this paper cites.
PredictiveNet: An energy-efficient convolutional neural network via zero prediction
Y. Lin, C. Sakr, Y. Kim, and N. Shanbhag · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Designing energy-efficient convolutional neural networks using energy-aware pruning
Tien-Ju Yang, Yu-Hsin Chen, and Vivienne Sze · 2017
Earlier work this paper cites.
Training deep neural networks with 8-bit floating point numbers
Naigang Wang, Jungwook Choi, Daniel Brand, Chia-Yu Chen, and Kailash Gopalakrishnan · 2018
Earlier work this paper cites.
Skipnet: Learning dynamic routing in convolutional networks
Xin Wang, Fisher Yu, Zi-Yi Dou, Trevor Darrell, and Joseph E Gonzalez · 2018
Cited alongside, same era.
Xianyan Jia, Shutao Song, Wei He, Yangzihao Wang, Haidong Rong, Feihu Zhou, Liqiang Xie, Zhenyu Guo, Yuanzhou Yang, Liwei Yu, et al · 2018
Cited alongside, same era.
Imagenet training in minutes
Yang You, Zhao Zhang, Cho-Jui Hsieh, James Demmel, and Kurt Keutzer · 2018
Cited alongside, same era.
Scalable methods for 8-bit training of neural networks
Ron Banner, Itay Hubara, Elad Hoffer, and Daniel Soudry · 2018
Cited alongside, same era.
signSGD: Compressed Optimisation for Non-Convex Problems
Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, and Animashree Anandkumar · 2018
Cited alongside, same era.
Channel gating neural networks
Weizhe Hua, Yuan Zhou, Christopher M De Sa, Zhiru Zhang, and G Edward Suh · 2019
Later among the works it cites.
Prunetrain: Fast neural network training by dynamic sparse model reconfiguration, 2019
Sangkug Lym, Esha Choukse, Siavash Zangeneh, Wei Wen, Sujay Sanghavi, and Mattan Erez · 2019
Later among the works it cites.
E2-train: Training state-of-the-art cnns with over 80% less energy
Yue Wang, Ziyu Jiang, Xiaohan Chen, Pengfei Xu, Yang Zhao, Yingyan Lin, and Zhangyang Wang · 2019
Later among the works it cites.
Accelerating deep learning by focusing on the biggest losers, 2019
Angela H. Jiang, Daniel L. K. Wong, Giulio Zhou, David G. Andersen, Jeffrey Dean, Gregory R. Ganger, Gauri Joshi, Michael Kaminksy, Michael Kozuch, Zachary C. Lipton, and Padmanabhan Pillai · 2019
Later among the works it cites.
On the spectral bias of neural networks
Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin, Fred Hamprecht, Yoshua Bengio, and Aaron Courville · 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…
Zuxuan Wu, Tushar Nagarajan, Abhishek Kumar, Steven Rennie, Larry S. Davis, Kristen Grauman, and Rogerio Feris · 2018
Cited alongside, same era.
Convolutional networks with adaptive inference graphs
Andreas Veit and Serge Belongie · 2018
Cited alongside, same era.
Gaternet: Dynamic filter selection in convolutional neural network via a dedicated global gating network, 2018
Zhourong Chen, Yang Li, Samy Bengio, and Si Si · 2018
Cited alongside, same era.
Dynamic channel pruning: Feature boosting and suppression
Xitong Gao, Yiren Zhao, Łukasz Dudziak, Robert Mullins, and Cheng-zhong Xu · 2018
Cited alongside, same era.
Bit fusion: Bit-level dynamically composable architecture for accelerating deep neural network
Hardik Sharma, Jongse Park, Naveen Suda, Liangzhen Lai, Benson Chau, Vikas Chandra, and Hadi Esmaeilzadeh · 2018
Cited alongside, same era.
Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
Cited alongside, same era.
Bit fusion: Bit-level dynamically composable architecture for accelerating deep neural networks
Hardik Sharma, Jongse Park, Naveen Suda, Liangzhen Lai, Benson Chau, Vikas Chandra, and Hadi Esmaeilzadeh · 2018
Cited alongside, same era.
Zhi-Qin John Xu, Yaoyu Zhang, Tao Luo, Yanyang Xiao, and Zheng Ma · 2019
Later among the works it cites.
Towards unified int8 training for convolutional neural network
Feng Zhu, Ruihao Gong, Fengwei Yu, Xianglong Liu, Yanfei Wang, Zhelong Li, Xiuqi Yang, and Junjie Yan · 2019
Later among the works it cites.
7.7 lnpu: A 25.3 tflops/w sparse deep-neural-network learning processor with fine-grained mixed precision of fp8-fp16
Jinsu Lee, Juhyoung Lee, Donghyeon Han, Jinmook Lee, Gwangtae Park, and Hoi-Jun Yoo · 2019
Later among the works it cites.
Haq: Hardware-aware automated quantization with mixed precision
Kuan Wang, Zhijian Liu, Yujun Lin, Ji Lin, and Song Han · 2019
Later among the works it cites.
fairseq: A fast, extensible toolkit for sequence modeling
Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli · 2019
Later among the works it cites.
Drawing early-bird tickets: Toward more efficient training of deep networks
Haoran You, Chaojian Li, Pengfei Xu, Yonggan Fu, Yue Wang, Xiaohan Chen, Yingyan Lin, Zhangyang Wang, and Richard G. Baraniuk · 2020
Closest in time.
Dual dynamic inference: Enabling more efficient, adaptive, and controllable deep inference
Y. Wang, J. Shen, T. K. Hu, P. Xu, T. Nguyen, R. Baraniuk, Z. Wang, and Y. Lin · 2020
Closest in time.
Fractional skipping: Towards finer-grained dynamic cnn inference
Jianghao Shen, Yonggan Fu, Yue Wang, Pengfei Xu, Zhangyang Wang, and Yingyan Lin · 2020
Closest in time.
Drq: dynamic region-based quantization for deep neural network acceleration
Zhuoran Song, Bangqi Fu, Feiyang Wu, Zhaoming Jiang, Li Jiang, Naifeng Jing, and Xiaoyao Liang · 2020
Closest in time.
HALO: Hardware-aware learning to optimize
Chaojian Li, Tianlong Chen, Haoran You, Zhangyang Wang, and Yingyan Lin · 2020
Closest in time.
1b-16b variable bit precision dnn processor for emotional hri system in mobile devices
Chang Hyeon Kim, Jin Mook Lee, Sang Hoon Kang, Sang Yeob Kim, Dong Seok Im, and Hoi Jun Yoo · 2020
Closest in time.
Yuhong Li, Cong Hao, Xiaofan Zhang, Xinheng Liu, Yao Chen, Jinjun Xiong, Wen-mei Hwu, and Deming Chen · 2020
Closest in time.
Xilinx zynq-7000 soc zc706 evaluation kit
Xilinx Inc · 2020
Closest in time.