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Deep learning as a means to inferencing has proliferated thanks to its versatility and ability to approach or exceed human-level accuracy.
Gradient-based learning applied to document recognition
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
IEEE Std 754-2008
IEEE standard for floating-point arithmetic · 2008
Earlier work this paper cites.
Non-Parametric Information-Theoretic Measures of One-Dimensional Distribution Functions from Continuous Time Series
P. D’Alberto and A. Dasdan · 2009
Earlier work this paper cites.
cuda-convnet
A. Krizhevsky · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
Training deep neural networks with low precision multiplications
M. Courbariaux, Y. Bengio, and J.-P. David · 2014
Earlier work this paper cites.
BVLC GoogLeNet
S. Guadarrama · 2014
Earlier work this paper cites.
1.1 computing’s energy problem (and what we can do about it)
M. Horowitz · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2014
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W. Dally · 2015
Cited alongside, same era.
Deep risidual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2015
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Hardware-oriented approximation of convolutional neural networks
P. Gysel, M. Motamedi, and S. Ghiasi · 2016
Cited alongside, same era.
TensorFlow
Google · 2017
Later among the works it cites.
MobileNets: Efficient convolutional neural networks for mobile vision applications
A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam · 2017
Later among the works it cites.
Quantization and training of neural networks for efficient integer- arithmetic-only inference
B. Jacob, S. Kligys, B. Chen, M. Zhu, M. Tang, A. Howard, H. Adam, , and D. Kalenichenko · 2017
Later among the works it cites.
8-bit inference with TensorRT
S. Migacz · 2017
Later among the works it cites.
MobileNet-Caffe
S. Yang · 2017
Later among the works it cites.
Flexibility: FPGAs and CAD in deep learning acceleration
G. R. Chui, A. C. Ling, D. Capalija, A. Bitar, and M. S. Abdelfattah · 2018
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Deep residual networks
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and < < 1MB model size
F. N. Iandola, M. W. Moskewicz, K. Ashraf, S. Han, W. J. Dally, and K. Keutzer · 2016
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8-bit dot-product acceleration
Y. Fu, E. Wu, and A. Sirasao · 2017
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B. Dally · 2018
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Ristretto: A framework for empirical study of resource-efficient inference in convolutional neural networks
P. Gysel, J. Pimentel, M. Motamedi, and S. Ghiasi · 2018
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