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State-of-the-art deep neural networks (DNNs) have hundreds of millions of connections and are both computationally and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources and power budgets.
S. J. Hanson and L. Y. Pratt, “Comparing biases for minimal network construction with back-propagation,” in
1989
Earlier work this paper cites.
Y. LeCun, J. S. Denker, S. A. Solla, R. E. Howard, and L. D. Jackel, “Optimal brain damage.” in
1989
Earlier work this paper cites.
V. Eijkhout,
1992
Earlier work this paper cites.
B. Hassibi, D. G. Stork
1993
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,”
1997
Earlier work this paper cites.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,”
1998
Earlier work this paper cites.
R. W. Vuduc, “Automatic performance tuning of sparse matrix kernels,” Ph.D. dissertation, UC Berkeley, 2003
2003
Earlier work this paper cites.
A. Graves and J. Schmidhuber, “Framewise phoneme classification with bidirectional lstm and other neural network architectures,”
2005
Earlier work this paper cites.
Ling Zhuo and Viktor K. Prasanna, “Sparse Matrix-Vector Multiplication on FPGAs,” in
2005
Earlier work this paper cites.
N. Bell and M. Garland, “Efficient sparse matrix-vector multiplication on cuda,” Nvidia Technical Report NVR-2008-004, Tech. Rep., 2008
2008
Earlier work this paper cites.
C. Farabet, C. Poulet, J. Y. Han, and Y. LeCun, “Cnp: An fpga-based processor for convolutional networks,” in
2009
Earlier work this paper cites.
N. Muralimanohar, R. Balasubramonian, and N. P. Jouppi, “Cacti 6.0: A tool to model large caches,”
2009
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in
2009
Earlier work this paper cites.
Bell, Nathan and Garland, Michael, “Implementing Sparse Matrix-vector Multiplication on Throughput-oriented Processors,” in
2009
Earlier work this paper cites.
T. Mikolov, M. Karafiát, L. Burget, J. Cernockỳ, and S. Khudanpur, “Recurrent neural network based language model.” in
2010
Cited alongside, same era.
V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in
2010
Cited alongside, same era.
Alexander Monakov and Anton Lokhmotov and Arutyun Avetisyan, “Automatically tuning sparse matrix-vector multiplication for GPU architectures,” in
2010
Cited alongside, same era.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in
2012
Cited alongside, same era.
A. Coates, B. Huval, T. Wang, D. Wu, B. Catanzaro, and N. Andrew, “Deep learning with cots hpc systems,” in
2013
Cited alongside, same era.
2014
Later among the works it cites.
J. Fowers and K. Ovtcharov and K. Strauss and E.S. Chung and G. Stitt, “A high memory bandwidth fpga accelerator for sparse matrix-vector multiplication,” in
2014
Later among the works it cites.
Z. Du, R. Fasthuber, T. Chen, P. Ienne, L. Li, T. Luo, X. Feng, Y. Chen, and O. Temam, “Shidiannao: shifting vision processing closer to the sensor,” in
2015
Later among the works it cites.
S. Han, J. Pool, J. Tran, and W. J. Dally, “Learning both weights and connections for efficient neural networks,” in
2015
Later among the works it cites.
2015
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2014
Cited alongside, same era.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,”
2014
Cited alongside, same era.
Y. Taigman, M. Yang, M. Ranzato, and L. Wolf, “Deepface: Closing the gap to human-level performance in face verification,” in
2014
Cited alongside, same era.
A. Karpathy and L. Fei-Fei, “Deep visual-semantic alignments for generating image descriptions,”
2014
Cited alongside, same era.
T. Chen, Z. Du, N. Sun, J. Wang, C. Wu, Y. Chen, and O. Temam, “Diannao: a small-footprint high-throughput accelerator for ubiquitous machine-learning,” in
2014
Cited alongside, same era.
Y. Chen, T. Luo, S. Liu, S. Zhang, L. He, J. Wang, L. Li, T. Chen, Z. Xu, N. Sun, and O. Temam, “Dadiannao: A machine-learning supercomputer,” in
2014
Cited alongside, same era.
Richard Dorrance and Fengbo Ren and Dejan Marković, “A Scalable Sparse Matrix-vector Multiplication Kernel for Energy-efficient Sparse-blas on FPGAs,” in
2014
Cited alongside, same era.
Later among the works it cites.
N. D. Lane and P. Georgiev, “Can deep learning revolutionize mobile sensing?” in
2015
Later among the works it cites.
A. Lavin, “Fast algorithms for convolutional neural networks,”
2015
Later among the works it cites.
C. Zhang, P. Li, G. Sun, Y. Guan, B. Xiao, and J. Cong, “Optimizing fpga-based accelerator design for deep convolutional neural networks,” in
2015
Later among the works it cites.
J. Qiu, J. Wang, S. Yao, K. Guo, B. Li, E. Zhou, J. Yu, T. Tang, N. Xu, S. Song, Y. Wang, and H. Yang, “Going deeper with embedded fpga platform for convolutional neural network,” in
2016
Closest in time.
A. Shafiee and et al., “ISAAC: A convolutional neural network accelerator with in-situ analog arithmetic in crossbars,”
2016
Closest in time.
S. Han, H. Mao, and W. J. Dally, “Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding,”
2016
Closest in time.
2016
Closest in time.
B. Reagen, P. Whatmough, R. Adolf, S. Rama, H. Lee, S. K. Lee, J. M. Hernández-Lobato, G.-Y. Wei, and D. Brooks, “Minerva: Enabling low-power, highly-accurate deep neural network accelerators,”
2016
Closest in time.