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State-of-the-art convolutional neural networks are enormously costly in both compute and memory, demanding massively parallel GPUs for execution.
Smoothing and differentiation of data by simplified least squares procedures
A. Savitzky and M. J. Golay · 1964
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Learning internal representations by error propagation
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ImageNet: A Large-Scale Hierarchical Image Database
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Maxout Networks
I. J. Goodfellow, D. Warde-Farley, M. Mirza, A. C. Courville, and Y. Bengio · 2013
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Learning Separable Filters
R. Rigamonti, A. Sironi, V. Lepetit, and P. Fua · 2013
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Speeding up Convolutional Neural Networks with Low Rank Expansions
M. Jaderberg, A. Vedaldi, and A. Zisserman · 2014
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Adam: A method for stochastic optimization
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Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan and A. Zisserman · 2014
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Expectation backpropagation: parameter-free training of multilayer neural networks with continuous or discrete weights
D. Soudry, I. Hubara, and R. Meir · 2014
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M. Courbariaux, Y. Bengio, and J.-P. David · 2015
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
S. Ioffe and C. Szegedy · 2015
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Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2015
Binarized Neural Networks
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M. Kim and P. Smaragdis · 2016
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Going Deeper with Embedded FPGA Platform for Convolutional Neural Network
J. Qiu, J. Wang, S. Yao, K. Guo, B. Li, E. Zhou, J. Yu, T. Tang, N. Xu, S. Song, et al · 2016
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XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks
M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi · 2016
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Throughput-Optimal OpenCL-based FPGA Accelerator for Large-Scale Convolutional Neural Networks
N. Suda, V. Chandra, G. Dasika, A. Mohanty, Y. Ma, S. Vrudhula, J.-s. Seo, and Y. Cao · 2016
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Going deeper with convolutions
C. Szegedy, Y. J. Wei Liu, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
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DecomposeMe: Simplifying ConvNets for End-to-End Learning
J. Alvarez and L. Petersson · 2016
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BinaryNet
M. Courbariaux · 2016
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C. Szegedy, S. Ioffe, and V. Vanhoucke · 2016
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Theano: A Python framework for fast computation of mathematical expressions
Theano Development Team · 2016
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Accelerating Binarized Convolutional Neural Networks with Software-Programmable FPGAs
R. Zhao, W. Song, W. Zhang, T. Xing, J.-H. Lin, M. Srivastava, R. Gupta, and Z. Zhang · 2017
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