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The recent popularity of deep neural networks (DNNs) has generated a lot of research interest in performing DNN-related computation efficiently.
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Gradient-based learning applied to document recognition
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Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu · 2002
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Vtune performance analyzer essentials
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A fast learning algorithm for deep belief nets
Geoffrey E Hinton, Simon Osindero, and Yee-Whye Teh · 2006
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Greedy layer-wise training of deep networks
Yoshua Bengio, Pascal Lamblin, Dan Popovici, and Hugo Larochelle · 2007
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Theano: A cpu and gpu math compiler in python
James Bergstra, Olivier Breuleux, Frédéric Bastien, Pascal Lamblin, Razvan Pascanu, Guillaume Desjardins, Joseph Turian, David Warde-Farley, and Yoshua Bengio · 2010
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The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2010
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Torch7: A matlab-like environment for machine learning
Ronan Collobert, Koray Kavukcuoglu, and Clément Farabet · 2011
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Large scale distributed deep networks
Jeffrey Dean, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Mark Mao, Andrew Senior, Paul Tucker, Ke Yang, Quoc V Le, et al · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2014
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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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Project adam: Building an efficient and scalable deep learning training system
Trishul M Chilimbi, Yutaka Suzue, Johnson Apacible, and Karthik Kalyanaraman · 2014
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Caffe: Convolutional architecture for fast feature embedding
Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell · 2014
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Scaling distributed machine learning with the parameter server
Mu Li, David G Andersen, Jun Woo Park, Alexander J Smola, Amr Ahmed, Vanja Josifovski, James Long, Eugene J Shekita, and Bor-Yiing Su · 2014
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le · 2014
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Intel math kernel library
Endong Wang, Qing Zhang, Bo Shen, Guangyong Zhang, Xiaowei Lu, Qing Wu, and Yajuan Wang · 2014
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Minerva: A scalable and highly efficient training platform for deep learning
Minjie Wang, Tianjun Xiao, Jianpeng Li, Jiaxing Zhang, Chuntao Hong, and Zheng Zhang · 2014
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An introduction to computational networks and the computational network toolkit
Dong Yu, Adam Eversole, Mike Seltzer, Kaisheng Yao, Zhiheng Huang, Brian Guenter, Oleksii Kuchaiev, Yu Zhang, Frank Seide, Huaming Wang, et al · 2014
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Fixed point optimization of deep convolutional neural networks for object recognition
Sajid Anwar, Kyuyeon Hwang, and Wonyong Sung · 2015
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The iwslt 2015 evaluation campaign
Mauro Cettolo, Jan Niehues, Sebastian Stüker, Luisa Bentivogli, Roldano Cattoni, and Marcello Federico · 2015
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Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems
Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, and Zheng Zhang · 2015
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Binaryconnect: Training deep neural networks with binary weights during propagations
Matthieu Courbariaux, Yoshua Bengio, and Jean-Pierre David · 2015
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Neuromorphic accelerators: A comparison between neuroscience and machine-learning approaches
Zidong Du, Daniel D Ben-Dayan Rubin, Yunji Chen, Liqiang He, Tianshi Chen, Lei Zhang, Chengyong Wu, and Olivier Temam · 2015
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Shidiannao: Shifting vision processing closer to the sensor
Zidong Du, Robert Fasthuber, Tianshi Chen, Paolo Ienne, Ling Li, Tao Luo, Xiaobing Feng, Yunji Chen, and Olivier Temam · 2015
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Backpropagation for energy-efficient neuromorphic computing
Steve K Esser, Rathinakumar Appuswamy, Paul Merolla, John V Arthur, and Dharmendra S Modha · 2015
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Song Han, Huizi Mao, and William J Dally · 2015
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Learning both weights and connections for efficient neural network
Song Han, Jeff Pool, John Tran, and William Dally · 2015
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Djinn and tonic: Dnn as a service and its implications for future warehouse scale computers
Johann Hauswald, Yiping Kang, Michael A Laurenzano, Quan Chen, Cheng Li, Trevor Mudge, Ronald G Dreslinski, Jason Mars, and Lingjia Tang · 2015
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An empirical evaluation of deep learning on highway driving
Brody Huval, Tao Wang, Sameep Tandon, Jeff Kiske, Will Song, Joel Pazhayampallil, Mykhaylo Andriluka, Pranav Rajpurkar, Toki Migimatsu, Royce Cheng-Yue, et al · 2015
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Effective approaches to attention-based neural machine translation
Minh-Thang Luong, Hieu Pham, and Christopher D Manning · 2015
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
Cited alongside, same era.
Librispeech: an asr corpus based on public domain audio books
Benchmarking state-of-the-art deep learning software tools
Shaohuai Shi, Qiang Wang, Pengfei Xu, and Xiaowen Chu · 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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Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al · 2016
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Cambricon-x: An accelerator for sparse neural networks
Shijin Zhang, Zidong Du, Lei Zhang, Huiying Lan, Shaoli Liu, Ling Li, Qi Guo, Tianshi Chen, and Yunji Chen · 2016
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Bit-pragmatic deep neural network computing
Jorge Albericio, Alberto Delmás, Patrick Judd, Sayeh Sharify, Gerard O’Leary, Roman Genov, and Andreas Moshovos · 2017
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Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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Chainer: a next-generation open source framework for deep learning
Seiya Tokui, Kenta Oono, Shohei Hido, and Justin Clayton · 2015
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Tensorflow: A system for large-scale machine learning
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al · 2016
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Fathom: reference workloads for modern deep learning methods
Robert Adolf, Saketh Rama, Brandon Reagen, Gu-Yeon Wei, and David Brooks · 2016
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Cnvlutin: ineffectual-neuron-free deep neural network computing
Jorge Albericio, Patrick Judd, Tayler Hetherington, Tor Aamodt, Natalie Enright Jerger, and Andreas Moshovos · 2016
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Fused-layer cnn accelerators
Manoj Alwani, Han Chen, Michael Ferdman, and Peter Milder · 2016
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Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Eyeriss: An energy-efficient reconfigurable accelerator for deep convolutional neural networks
Yu-Hsin Chen, Tushar Krishna, Joel S Emer, and Vivienne Sze · 2017
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A downsampled variant of imagenet as an alternative to the cifar datasets
Patryk Chrabaszcz, Ilya Loshchilov, and Frank Hutter · 2017
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Understanding and optimizing asynchronous low-precision stochastic gradient descent
Christopher De Sa, Matthew Feldman, Christopher Ré, and Kunle Olukotun · 2017
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C ir cnn: accelerating and compressing deep neural networks using block-circulant weight matrices
Caiwen Ding, Siyu Liao, Yanzhi Wang, Zhe Li, Ning Liu, Youwei Zhuo, Chao Wang, Xuehai Qian, Yu Bai, Geng Yuan, et al · 2017
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Tetris: Scalable and efficient neural network acceleration with 3d memory
Mingyu Gao, Jing Pu, Xuan Yang, Mark Horowitz, and Christos Kozyrakis · 2017
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Accurate, large minibatch sgd: Training imagenet in 1 hour
Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He · 2017
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Fpga-based accelerator for long short-term memory recurrent neural networks
Yijin Guan, Zhihang Yuan, Guangyu Sun, and Jason Cong · 2017
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Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
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Neural collaborative filtering
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua · 2017
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Sockeye: A toolkit for neural machine translation
Felix Hieber, Tobias Domhan, Michael Denkowski, David Vilar, Artem Sokolov, Ann Clifton, and Matt Post · 2017
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Gaia: Geo-distributed machine learning approaching lan speeds
Kevin Hsieh, Aaron Harlap, Nandita Vijaykumar, Dimitris Konomis, Gregory R Ganger, Phillip B Gibbons, and Onur Mutlu · 2017
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In-datacenter performance analysis of a tensor processing unit
Norman P Jouppi, Cliff Young, Nishant Patil, David Patterson, Gaurav Agrawal, Raminder Bajwa, Sarah Bates, Suresh Bhatia, Nan Boden, Al Borchers, et al · 2017
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Flexflow: A flexible dataflow accelerator architecture for convolutional neural networks
Wenyan Lu, Guihai Yan, Jiajun Li, Shijun Gong, Yinhe Han, and Xiaowei Li · 2017
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Scnn: An accelerator for compressed-sparse convolutional neural networks
Angshuman Parashar, Minsoo Rhu, Anurag Mukkara, Antonio Puglielli, Rangharajan Venkatesan, Brucek Khailany, Joel Emer, Stephen W Keckler, and William J Dally · 2017
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Scale-out acceleration for machine learning
Jongse Park, Hardik Sharma, Divya Mahajan, Joon Kyung Kim, Preston Olds, and Hadi Esmaeilzadeh · 2017
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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
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Deepxplore: Automated whitebox testing of deep learning systems
Kexin Pei, Yinzhi Cao, Junfeng Yang, and Suman Jana · 2017
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Sc-dcnn: Highly-scalable deep convolutional neural network using stochastic computing
Ao Ren, Zhe Li, Caiwen Ding, Qinru Qiu, Yanzhi Wang, Ji Li, Xuehai Qian, and Bo Yuan · 2017
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Pipelayer: A pipelined reram-based accelerator for deep learning
Linghao Song, Xuehai Qian, Hai Li, and Yiran Chen · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Scaledeep: A scalable compute architecture for learning and evaluating deep networks
Swagath Venkataramani, Ashish Ranjan, Subarno Banerjee, Dipankar Das, Sasikanth Avancha, Ashok Jagannathan, Ajaya Durg, Dheemanth Nagaraj, Bharat Kaul, Pradeep Dubey, et al · 2017
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Tux2: Distributed graph computation for machine learning
Wencong Xiao, Jilong Xue, Youshan Miao, Zhen Li, Cheng Chen, Ming Wu, Wei Li, and Lidong Zhou · 2017
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The microsoft 2016 conversational speech recognition system
Wayne Xiong, Jasha Droppo, Xuedong Huang, Frank Seide, Mike Seltzer, Andreas Stolcke, Dong Yu, and Geoffrey Zweig · 2017
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Yang You, Zhao Zhang, C Hsieh, James Demmel, and Kurt Keutzer · 2017
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Scalpel: Customizing dnn pruning to the underlying hardware parallelism
Jiecao Yu, Andrew Lukefahr, David Palframan, Ganesh Dasika, Reetuparna Das, and Scott Mahlke · 2017
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