Built on

C. Zhang, P. Li, G. Sun, Y. Guan, B. Xiao, and J. Cong, “Optimizing fpga-based accelerator design for deep convolutional neural networks,” in Proceedings of the 2015 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays
2015
Earlier work this paper cites.
K. Ovtcharov, O. Ruwase, J.-Y. Kim, J. Fowers, K. Strauss, and E. S. Chung, “Accelerating deep convolutional neural networks using specialized hardware,” Microsoft Research Whitepaper
2015
Earlier work this paper cites.
Y.-D. Kim, E. Park, S. Yoo, T. Choi, L. Yang, and D. Shin, “Compression of deep convolutional neural networks for fast and low power mobile applications,” arXiv preprint arXiv:1511.06530
Original
2015
Earlier work this paper cites.
X. Zhang, J. Zou, X. Ming, K. He, and J. Sun, “Efficient and accurate approximations of nonlinear convolutional networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2015
Earlier work this paper cites.
S. Han, H. Mao, and W. J. Dally, “Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding,” arXiv preprint arXiv:1510.00149
Original
2015
Earlier work this paper cites.
B. Liu, M. Wang, H. Foroosh, M. Tappen, and M. Pensky, “Sparse convolutional neural networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2015
Earlier work this paper cites.
F. Chollet et al
2015
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Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature
2015
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M. Courbariaux, Y. Bengio, and J.-P. David, “Binaryconnect: Training deep neural networks with binary weights during propagations,” in Advances in Neural Information Processing Systems
2015
Earlier work this paper cites.

Similar

N. Suda, V. Chandra, G. Dasika, A. Mohanty, Y. Ma, S. Vrudhula, J.-s. Seo, and Y. Cao, “Throughput-optimized opencl-based fpga accelerator for large-scale convolutional neural networks,” in Proceedings of the 2016 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays
2016
Cited alongside, same era.
I. Hubara, M. Courbariaux, D. Soudry, R. El-Yaniv, and Y. Bengio, “Quantized neural networks: Training neural networks with low precision weights and activations,” arXiv preprint arXiv:1609.07061
Original
2016
Cited alongside, same era.
W. Wen, C. Wu, Y. Wang, Y. Chen, and H. Li, “Learning structured sparsity in deep neural networks,” in Advances in Neural Information Processing Systems
2016
Cited alongside, same era.
M. Courbariaux, I. Hubara, D. Soudry, R. El-Yaniv, and Y. Bengio, “Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1,” arXiv preprint arXiv:1602.02830
Original
2016
Cited alongside, same era.

Then

X. Zhang, X. Zhou, M. Lin, and J. Sun, “Shufflenet: An extremely efficient convolutional neural network for mobile devices,” arXiv preprint arXiv:1707.01083
Original
2017
Closest in time.
A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, “Mobilenets: Efficient convolutional neural networks for mobile vision applications,” arXiv preprint arXiv:1704.04861
Original
2017
Closest in time.
M. Samragh, M. Ghasemzadeh, and F. Koushanfar, “Customizing neural networks for efficient fpga implementation,” in Field-Programmable Custom Computing Machines (FCCM), 2017 IEEE 25th Annual International Symposium on
2017
Closest in time.
Y. Umuroglu, N. J. Fraser, G. Gambardella, M. Blott, P. Leong, M. Jahre, and K. Vissers, “Finn: A framework for fast, scalable binarized neural network inference,” in Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays
2017
Closest in time.

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Reading the bibliography…

2017

ReBNet: Residual Binarized Neural Network

Ghasemzadeh, Mohammad, Samragh, Mohammad, Koushanfar, Farinaz

Understand

This paper proposes ReBNet, an end-to-end framework for training reconfigurable binary neural networks on software and developing efficient accelerators for execution on FPGA.

Binary neural networks offer an intriguing opportunity for deploying large-scale deep learning models on resource-constrained devices.
Binarization reduces the memory footprint and replaces the power-hungry matrix-multiplication with light-weight XnorPopcount operations.
However, binary networks suffer from a degraded accuracy compared to their fixed-point counterparts.

Built on

C. Zhang, P. Li, G. Sun, Y. Guan, B. Xiao, and J. Cong, “Optimizing fpga-based accelerator design for deep convolutional neural networks,” in Proceedings of the 2015 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays
2015
Earlier work this paper cites.
K. Ovtcharov, O. Ruwase, J.-Y. Kim, J. Fowers, K. Strauss, and E. S. Chung, “Accelerating deep convolutional neural networks using specialized hardware,” Microsoft Research Whitepaper
2015
Earlier work this paper cites.
Y.-D. Kim, E. Park, S. Yoo, T. Choi, L. Yang, and D. Shin, “Compression of deep convolutional neural networks for fast and low power mobile applications,” arXiv preprint arXiv:1511.06530
Original
2015
Earlier work this paper cites.
X. Zhang, J. Zou, X. Ming, K. He, and J. Sun, “Efficient and accurate approximations of nonlinear convolutional networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2015
Earlier work this paper cites.
S. Han, H. Mao, and W. J. Dally, “Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding,” arXiv preprint arXiv:1510.00149
Original
2015
Earlier work this paper cites.
B. Liu, M. Wang, H. Foroosh, M. Tappen, and M. Pensky, “Sparse convolutional neural networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2015
Earlier work this paper cites.
F. Chollet et al
2015
Earlier work this paper cites.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature
2015
Earlier work this paper cites.
M. Courbariaux, Y. Bengio, and J.-P. David, “Binaryconnect: Training deep neural networks with binary weights during propagations,” in Advances in Neural Information Processing Systems
2015
Earlier work this paper cites.

Similar

N. Suda, V. Chandra, G. Dasika, A. Mohanty, Y. Ma, S. Vrudhula, J.-s. Seo, and Y. Cao, “Throughput-optimized opencl-based fpga accelerator for large-scale convolutional neural networks,” in Proceedings of the 2016 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays
2016
Cited alongside, same era.
I. Hubara, M. Courbariaux, D. Soudry, R. El-Yaniv, and Y. Bengio, “Quantized neural networks: Training neural networks with low precision weights and activations,” arXiv preprint arXiv:1609.07061
Original
2016
Cited alongside, same era.
W. Wen, C. Wu, Y. Wang, Y. Chen, and H. Li, “Learning structured sparsity in deep neural networks,” in Advances in Neural Information Processing Systems
2016
Cited alongside, same era.
M. Courbariaux, I. Hubara, D. Soudry, R. El-Yaniv, and Y. Bengio, “Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1,” arXiv preprint arXiv:1602.02830
Original
2016
Cited alongside, same era.

Then

X. Zhang, X. Zhou, M. Lin, and J. Sun, “Shufflenet: An extremely efficient convolutional neural network for mobile devices,” arXiv preprint arXiv:1707.01083
Original
2017
Closest in time.
A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, “Mobilenets: Efficient convolutional neural networks for mobile vision applications,” arXiv preprint arXiv:1704.04861
Original
2017
Closest in time.
M. Samragh, M. Ghasemzadeh, and F. Koushanfar, “Customizing neural networks for efficient fpga implementation,” in Field-Programmable Custom Computing Machines (FCCM), 2017 IEEE 25th Annual International Symposium on
2017
Closest in time.
Y. Umuroglu, N. J. Fraser, G. Gambardella, M. Blott, P. Leong, M. Jahre, and K. Vissers, “Finn: A framework for fast, scalable binarized neural network inference,” in Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays
2017
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi, “Xnor-net: Imagenet classification using binary convolutional neural networks,” in European Conference on Computer Vision

2016

Cited alongside, same era.

S. Zhou, Y. Wu, Z. Ni, X. Zhou, H. Wen, and Y. Zou, “Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients,” arXiv preprint arXiv:1606.06160

Original

2016

Cited alongside, same era.

S. Han, X. Liu, H. Mao, J. Pu, A. Pedram, M. A. Horowitz, and W. J. Dally, “Eie: efficient inference engine on compressed deep neural network,” in Proceedings of the 43rd International Symposium on Computer Architecture

2016

Cited alongside, same era.

J. Wu, C. Leng, Y. Wang, Q. Hu, and J. Cheng, “Quantized convolutional neural networks for mobile devices,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

2016

Cited alongside, same era.

D. Li, X. Wang, and D. Kong, “Deeprebirth: Accelerating deep neural network execution on mobile devices,” arXiv preprint arXiv:1708.04728

Original

2017

Cited alongside, same era.

W. Tang, G. Hua, and L. Wang, “How to train a compact binary neural network with high accuracy?,” in AAAI

2017

Closest in time.

H. Alemdar, V. Leroy, A. Prost-Boucle, and F. Pétrot, “Ternary neural networks for resource-efficient ai applications,” in Neural Networks (IJCNN), 2017 International Joint Conference on

2017

Closest in time.

N. Mellempudi, A. Kundu, D. Mudigere, D. Das, B. Kaul, and P. Dubey, “Ternary neural networks with fine-grained quantization,” arXiv preprint arXiv:1705.01462

Original

2017

Closest in time.

Xilinx, “Vivado.” https://www.xilinx.com/products/design-tools/vivado.html , 2017

2017

Closest in time.

M. Nazemi, A. E. Eshratifar, and M. Pedram, “A hardware-friendly algorithm for scalable training and deployment of dimensionality reduction models on FPGA,” in Proceedings of the 19th IEEE International Symposium on Quality Electronic Design

2018

Closest in time.