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In 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI 17). USENIX Association, Boston, MA, 629--647. https:\/\/www.usenix.org\/conference\/nsdi17\/technical-sessions\/presentation\/hsieh"},{"key":"e_1_3_2_1_25_1","volume-title":"Kingma and Jimmy Ba","author":"Diederik","year":"2014","unstructured":"Diederik P. Kingma and Jimmy Ba . 2014 . Adam : A Method for Stochastic Optimization . arxiv: 1412.6980 [cs.LG] Diederik P. Kingma and Jimmy Ba. 2014. Adam: A Method for Stochastic Optimization. arxiv: 1412.6980 [cs.LG]"},{"key":"e_1_3_2_1_26_1","unstructured":"Alex Krizhevsky Geoffrey Hinton etal 2009. Learning multiple layers of features from tiny images. (2009).  Alex Krizhevsky Geoffrey Hinton et al. 2009. Learning multiple layers of features from tiny images. (2009)."},{"key":"e_1_3_2_1_27_1","unstructured":"Alex Krizhevsky Vinod Nair and Geoffrey Hinton. [n.d.]. CIFAR-10 (Canadian Institute for Advanced Research). ( [n. d.]). http:\/\/www.cs.toronto.edu\/ kriz\/cifar.html  Alex Krizhevsky Vinod Nair and Geoffrey Hinton. [n.d.]. CIFAR-10 (Canadian Institute for Advanced Research). ( [n. d.]). http:\/\/www.cs.toronto.edu\/ kriz\/cifar.html"},{"volume-title":"Advances in Neural Information Processing Systems 25","author":"Krizhevsky Alex","key":"e_1_3_2_1_28_1","unstructured":"Alex Krizhevsky , Ilya Sutskever , and Geoffrey E Hinton . 2012. ImageNet Classification with Deep Convolutional Neural Networks . In Advances in Neural Information Processing Systems 25 , F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger (Eds.). Curran Associates, Inc. , 1097--1105. http:\/\/papers.nips.cc\/paper\/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012. ImageNet Classification with Deep Convolutional Neural Networks. In Advances in Neural Information Processing Systems 25, F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger (Eds.). Curran Associates, Inc., 1097--1105. http:\/\/papers.nips.cc\/paper\/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf"},{"key":"e_1_3_2_1_29_1","volume-title":"Asynchronous decentralized parallel stochastic gradient descent. arXiv preprint arXiv:1710.06952","author":"Lian Xiangru","year":"2017","unstructured":"Xiangru Lian , Wei Zhang , Ce Zhang , and Ji Liu . 2017. Asynchronous decentralized parallel stochastic gradient descent. arXiv preprint arXiv:1710.06952 ( 2017 ). Xiangru Lian, Wei Zhang, Ce Zhang, and Ji Liu. 2017. Asynchronous decentralized parallel stochastic gradient descent. arXiv preprint arXiv:1710.06952 (2017)."},{"key":"e_1_3_2_1_30_1","volume-title":"Dally","author":"Lin Yujun","year":"2017","unstructured":"Yujun Lin , Song Han , Huizi Mao , Yu Wang , and William J . Dally . 2017 . Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training . arxiv: 1712.01887 [cs.CV] Yujun Lin, Song Han, Huizi Mao, Yu Wang, and William J. Dally. 2017. Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training. arxiv: 1712.01887 [cs.CV]"},{"key":"e_1_3_2_1_31_1","volume-title":"Wright","author":"Niu Feng","year":"2011","unstructured":"Feng Niu , Benjamin Recht , Christopher Re , and Stephen J . Wright . 2011 . HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent . arxiv: 1106.5730 [math.OC] Feng Niu, Benjamin Recht, Christopher Re, and Stephen J. Wright. 2011. HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent. arxiv: 1106.5730 [math.OC]"},{"key":"e_1_3_2_1_32_1","volume-title":"Hai Li, Yiran Chen, and Pradeep Dubey.","author":"Park Jongsoo","year":"2016","unstructured":"Jongsoo Park , Sheng Li , Wei Wen , Ping Tak Peter Tang , Hai Li, Yiran Chen, and Pradeep Dubey. 2016 . Faster CNNs with Direct Sparse Convolutions and Guided Pruning . arxiv: 1608.01409 [cs.CV] Jongsoo Park, Sheng Li, Wei Wen, Ping Tak Peter Tang, Hai Li, Yiran Chen, and Pradeep Dubey. 2016. Faster CNNs with Direct Sparse Convolutions and Guided Pruning. arxiv: 1608.01409 [cs.CV]"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"crossref","unstructured":"Mohammad Rastegari Vicente Ordonez Joseph Redmon and Ali Farhadi. 2016. XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks. In ECCV.  Mohammad Rastegari Vicente Ordonez Joseph Redmon and Ali Farhadi. 2016. XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks. In ECCV.","DOI":"10.1007\/978-3-319-46493-0_32"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"crossref","unstructured":"Frank Seide Hao Fu Jasha Droppo Gang Li and Dong Yu. 2014. 1-Bit Stochastic Gradient Descent and Application to Data-Parallel Distributed Training of Speech DNNs. In Interspeech 2014 interspeech 2014 ed.). https:\/\/www.microsoft.com\/en-us\/research\/publication\/1-bit-stochastic-gradient-descent-and-application-to-data-parallel-distributed-training-of-speech-dnns\/  Frank Seide Hao Fu Jasha Droppo Gang Li and Dong Yu. 2014. 1-Bit Stochastic Gradient Descent and Application to Data-Parallel Distributed Training of Speech DNNs. In Interspeech 2014 interspeech 2014 ed.). https:\/\/www.microsoft.com\/en-us\/research\/publication\/1-bit-stochastic-gradient-descent-and-application-to-data-parallel-distributed-training-of-speech-dnns\/","DOI":"10.21437\/Interspeech.2014-274"},{"key":"e_1_3_2_1_35_1","unstructured":"Karen Simonyan and Andrew Zisserman. 2014a. Very Deep Convolutional Networks for Large-Scale Image Recognition. arxiv: 1409.1556 [cs.CV]  Karen Simonyan and Andrew Zisserman. 2014a. Very Deep Convolutional Networks for Large-Scale Image Recognition. arxiv: 1409.1556 [cs.CV]"},{"key":"e_1_3_2_1_36_1","volume-title":"Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556","author":"Simonyan Karen","year":"2014","unstructured":"Karen Simonyan and Andrew Zisserman . 2014b. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 ( 2014 ). Karen Simonyan and Andrew Zisserman. 2014b. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"crossref","unstructured":"Nikko Strom. 2015. Scalable distributed DNN training using commodity GPU cloud computing. In INTERSPEECH.  Nikko Strom. 2015. Scalable distributed DNN training using commodity GPU cloud computing. In INTERSPEECH.","DOI":"10.21437\/Interspeech.2015-354"},{"key":"e_1_3_2_1_38_1","unstructured":"Wei Wen Cong Xu Feng Yan Chunpeng Wu Yandan Wang Yiran Chen and Hai Li. 2017. TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning. arxiv: 1705.07878 [cs.LG]  Wei Wen Cong Xu Feng Yan Chunpeng Wu Yandan Wang Yiran Chen and Hai Li. 2017. TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning. arxiv: 1705.07878 [cs.LG]"},{"key":"e_1_3_2_1_39_1","volume-title":"Training and Inference with Integers in Deep Neural Networks. arxiv","author":"Wu Shuang","year":"1802","unstructured":"Shuang Wu , Guoqi Li , Feng Chen , and Luping Shi . 2018. Training and Inference with Integers in Deep Neural Networks. arxiv : 1802 .04680 [cs.LG] Shuang Wu, Guoqi Li, Feng Chen, and Luping Shi. 2018. Training and Inference with Integers in Deep Neural Networks. arxiv: 1802.04680 [cs.LG]"},{"key":"e_1_3_2_1_40_1","unstructured":"Shuxin Zheng Qi Meng Taifeng Wang Wei Chen Nenghai Yu Zhi-Ming Ma and Tie-Yan Liu. 2016. Asynchronous Stochastic Gradient Descent with Delay Compensation. arxiv: 1609.08326 [cs.LG]  Shuxin Zheng Qi Meng Taifeng Wang Wei Chen Nenghai Yu Zhi-Ming Ma and Tie-Yan Liu. 2016. Asynchronous Stochastic Gradient Descent with Delay Compensation. arxiv: 1609.08326 [cs.LG]"},{"key":"e_1_3_2_1_41_1","volume-title":"DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients. CoRR","author":"Zhou Shuchang","year":"2016","unstructured":"Shuchang Zhou , Yuxin Wu , Zekun Ni , Xinyu Zhou , He Wen , and Yuheng Zou . 2016. DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients. CoRR , Vol. abs\/ 1606 .06160 ( 2016 ). http:\/\/arxiv.org\/abs\/1606.06160 Shuchang Zhou, Yuxin Wu, Zekun Ni, Xinyu Zhou, He Wen, and Yuheng Zou. 2016. DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients. CoRR, Vol. abs\/1606.06160 (2016). http:\/\/arxiv.org\/abs\/1606.06160"},{"key":"e_1_3_2_1_42_1","volume-title":"Proceedings of the 20th international conference on machine learning (icml-03)","author":"Zinkevich Martin","year":"2003","unstructured":"Martin Zinkevich . 2003 . Online convex programming and generalized infinitesimal gradient ascent . In Proceedings of the 20th international conference on machine learning (icml-03) . 928--936. Martin Zinkevich. 2003. Online convex programming and generalized infinitesimal gradient ascent. 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