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Convolutional Neural Networks (CNNs) have emerged as a fundamental technology for machine learning.
Framewise Phoneme Classification With Bidirectional LSTM and Other Neural Network Architectures
A. Graves and J. Schmidhuber · 2005
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G. Martin and G. Smith · 2009
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Natural Language Processing (Almost) From Scratch
R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, and P. Kuksa · 2011
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ImageNet Classification with Deep Convolutional Neural Networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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DianNao: A Small-footprint High-throughput Accelerator for Ubiquitous Machine-learning
T. Chen, Z. Du, N. Sun, J. Wang, C. Wu, Y. Chen, and O. Temam · 2014
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Minimizing Computation in Convolutional Neural Networks
J. Cong and B. Xiao · 2014
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Deep Speech: Scaling Up End-To-End Speech Recognition
A. Hannun, C. Case, J. Casper, B. Catanzaro, G. Diamos, E. Elsen, R. Prenger, S. Satheesh, S. Sengupta, A. Coates, and A. Y. Ng · 2014
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Deep Speech 2: End-To-End Speech Recognition in English and Mandarin
D. Amodei, R. Anubhai, E. Battenberg, C. Case, J. Casper, B. Catanzaro, J. Chen, M. Chrzanowski, A. Coates, G. Diamos, E. Elsen, J. Engel, L. Fan, C. Fougner, T. Han, A. Hannun, B. Jun, P. LeGresley, L. Lin, S. Narang, A. Ng, S. Ozair, R. Prenger, J. Raiman, S. Satheesh, D. Seetapun, S. Sengupta, Y. Wang, Z. Wang, C. Wang, B. Xiao, D. Yogatama, J. Zhan, and Z. Zhu · 2015
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ShiDianNao: Shifting Vision Processing Closer to the Sensor
Z. Du, R. Fasthuber, T. Chen, P. Ienne, L. Li, T. Luo, X. Feng, Y. Chen, and O. Temam · 2015
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S. Han, H. Mao, and W. J. Dally · 2015
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Learning Both Weights and Connections for Efficient Neural Networks
S. Han, J. Pool, J. Tran, and W. J. Dally · 2015
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Deep Residual Learning for Image Recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2015
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Deep Learning
Y. LeCun, Y. Bengio, and G. Hinton · 2015
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Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan and A. Zisserman · 2015
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Going Deeper with Convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
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Cnvlutin: Ineffectual-Neuron-Free Deep Convolutional Neural Network Computing
EIE: Efficient Inference Engine on Compressed Deep Neural Network
S. Han, X. Liu, H. Mao, J. Pu, A. Pedram, M. Horowitz, and B. Dally · 2016
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Deep Networks with Stochastic Depth
G. Huang, Y. Sun, Z. Liu, D. Sedra, and K. Weinberger · 2016
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http://image-net.org, 2016
ImageNet · 2016
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https://developer.nvidia.com/cudnn
NVIDIA cuDNN · 2016
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Minerva: Enabling Low-Power, High-Accuracy Deep Neural Network Accelerators
B. Reagen, P. Whatmough, R. Adolf, S. Rama, H. Lee, S. Lee, J. M. H. Lobato, G.-Y. Wei, and D. Brooks · 2016
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vDNN: Virtualized Deep Neural Networks for Scalable, Memory-Efficient Neural Network Design
M. Rhu, N. Gimelshein, J. Clemons, A. Zulfiqar, and S. W. Keckler · 2016
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J. Albericio, P. Judd, T. Hetherington, T. Aamodt, N. E. Jerger, and A. Moshovos · 2016
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http://caffe.berkeleyvision.org
Caffe · 2016
Cited alongside, same era.
Eyeriss: A Spatial Architecture for Energy-Efficient Dataflow for Convolutional Neural Networks
Y.-H. Chen, J. Emer, and V. Sze · 2016
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Eyeriss: An Energy-efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks
Y.-H. Chen, T. Krishna, J. Emer, and V. Sze · 2016
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Persistent RNNs: Stashing Recurrent Weights On-Chip
G. Diamos, S. Sengupta, B. Catanzaro, M. Chrzanowski, A. Coates, E. Elsen, J. Engel, A. Hannun, and S. Satheesh · 2016
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Microsoft Neural Net Shows Deep Learning Can Get Way Deeper, 2016
Wired · 2016
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Cambricon-X: An Accelerator for Sparse Neural Networks
S. Zhang, Z. Du, L. Zhang, H. Lan, S. Liu, L. Li, Q. Guo, T. Chen, and Y. Chen · 2016
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https://github.com/BVLC/caffe/wiki/Model-Zoo
Caffe Model Zoo · 2017
Closest in time.
https://www.mentor.com/hls-lp/catapult-high-level-synthesis
Catapult High-Level Synthesis · 2017
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