Built on

Y. LeCun, J. S. Denker, and S. A. Solla, “Optimal brain damage,” in Proc. Advances in Neural Inf. Process. Syst. , 1990, pp. 598–605
1990
Earlier work this paper cites.
B. Hassibi and D. G. Stork, “Second order derivatives for network pruning: Optimal brain surgeon,” in Proc. Advances in Neural Inf. Process. Syst. , 1993, pp. 164–171
1993
Earlier work this paper cites.
B. Hassibi, D. G. Stork, and G. J. Wolff, “Optimal brain surgeon and general network pruning,” in Proc. Int. Conf. Neural Networks , 1993, pp. 293–299
1993
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in Proc. Advances in Neural Inf. Process. Syst. , 2012, pp. 1097–1105
2012
Earlier work this paper cites.
P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun, “OverFeat: Integrated recognition, localization and detection using convolutional networks,” in Proc. Int. Conf. Learning Representations , 2014. [Online]. Available: http://arxiv.org/abs/1312.6229
Original
2014
Earlier work this paper cites.
R. Grishick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” in Proc. Comput. Vision and Pattern Recognition , 2014, pp. 580–587
2014
Earlier work this paper cites.
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 Proc. Int. Conf. Architectural Support for Programming Languages and Operating Syst. , 2014, pp. 269–283
2014
Earlier work this paper cites.
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrel. (2014) Caffe: Convolutional architecture for fast feature embedding. [Online]. Available: http://arxiv.org/abs/1408.5093
Original
2014
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in Proc. Int. Conf. Learning Representations , 2015. [Online]. Available: http://arxiv.org/abs/1409.1556
Original
2015
Earlier work this paper cites.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proc. Comput. Vision and Pattern Recognition , 2015, pp. 1–9. [Online]. Available: http://arxiv.org/abs/1409.4842
Original
2015
Earlier work this paper cites.
R. Girshick, “Fast R-CNN,” in Proc. Int. Conf. Comput. Vision , 2015, pp. 1440–1448. [Online]. Available: http://arxiv.org/abs/1504.08083
Original
2015
Earlier work this paper cites.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” in Proc. Advances in Neural Inf. Process. Syst. , 2015, pp. 1–9
2015
Earlier work this paper cites.
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proc. Comput. Vision and Pattern Recognition , 2015. [Online]. Available: http://arxiv.org/abs/1411.4038
Original
2015
Earlier work this paper 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 Proc. ACM/SIGDA Int. Symp. Field Programmable Gate Arrays , 2015, pp. 161–170
2015
Earlier work this paper cites.
S. Han, J. Pool, J. Tran, and W. J. Dally, “Learning both weights and connections for efficient neural networks,” in Proc. Advances in Neural Inf. Process. Syst. , 2015, pp. 1135–1143. [Online]. Available: http://arxiv.org/abs/1506.02626
Original
2015
Earlier work this paper cites.

Similar

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, Z. Ma, S.a dn Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet large scale visual recognition challenge,” Int. J. Comput. Vision , vol. 115, no. 3, pp. 211–252, Dec. 2015
2015
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. Comput. Vision and Pattern Recognition , 2016, pp. 770–778. [Online]. Available: http://arxiv.org/abs/1512.03385
Original
2016
Cited alongside, same era.
F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, and K. Keutzer. (2016) SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and < < 0.5MB model size. [Online]. Available: http://arxiv.org/abs/1602.07360
Original
2016
Cited alongside, same era.
J. Albericio, P. Judd, T. Hetherington, T. Aamodt, N. E. Jerger, and A. Moshovos, “Cnvlutin: Ineffectual-neuron-free deep neural network computing,” in Proc. Int. Symp. Comput. Architecture , 2016, pp. 1–13
2016

Then

Y. He, X. Zhang, and J. Sun, “Channel pruning for accelerating very deep neural networks,” in Proc. Int. Conf. Comput. Vision , 2017, pp. 1398–1406. [Online]. Available: http://arxiv.org/abs/1707.06168
Original
2017
Later among the works it cites.
J. Yu, A. Lukefahr, D. Palframan, G. Dasika, R. Das, and S. Mahlke, “Scalpel: Customizing DNN pruning to the underlying hardware parallelism,” in Proc. Int. Symp. Comput. Architecture , 2017, pp. 548–560
2017
Later among the works it cites.
P. Molchanov, S. Tyree, T. Karras, T. Aila, and J. Kautz, “Pruning convolutional neural networks for resource efficient inference,” in Proc. Int. Conf. Learning Representations , 2017. [Online]. Available: http://arxiv.org/abs/1611.06440
Original
2017
Later among the works it cites.
N. Yu, S. Qiu, X. Hu, and J. Li, “Accelerating convolutional neural networks by group-wise 2D-filter pruning,” in Proc. Int. Joint Conf. Neural Networks , 2017, pp. 2502–2509
2017
Later among the works it cites.

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2018

Accelerator-Aware Pruning for Convolutional Neural Networks

Kang, Hyeong-Ju

Understand

Convolutional neural networks have shown tremendous performance capabilities in computer vision tasks, but their excessive amounts of weight storage and arithmetic operations prevent them from being adopted in embedded environments.

One of the solutions involves pruning, where certain unimportant weights are forced to have a value of zero.
Many pruning schemes have been proposed, but these have mainly focused on the number of pruned weights.
Previous pruning schemes scarcely considered ASIC or FPGA accelerator architectures.

Built on

Y. LeCun, J. S. Denker, and S. A. Solla, “Optimal brain damage,” in Proc. Advances in Neural Inf. Process. Syst. , 1990, pp. 598–605
1990
Earlier work this paper cites.
B. Hassibi and D. G. Stork, “Second order derivatives for network pruning: Optimal brain surgeon,” in Proc. Advances in Neural Inf. Process. Syst. , 1993, pp. 164–171
1993
Earlier work this paper cites.
B. Hassibi, D. G. Stork, and G. J. Wolff, “Optimal brain surgeon and general network pruning,” in Proc. Int. Conf. Neural Networks , 1993, pp. 293–299
1993
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in Proc. Advances in Neural Inf. Process. Syst. , 2012, pp. 1097–1105
2012
Earlier work this paper cites.
P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun, “OverFeat: Integrated recognition, localization and detection using convolutional networks,” in Proc. Int. Conf. Learning Representations , 2014. [Online]. Available: http://arxiv.org/abs/1312.6229
Original
2014
Earlier work this paper cites.
R. Grishick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” in Proc. Comput. Vision and Pattern Recognition , 2014, pp. 580–587
2014
Earlier work this paper cites.
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 Proc. Int. Conf. Architectural Support for Programming Languages and Operating Syst. , 2014, pp. 269–283
2014
Earlier work this paper cites.
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrel. (2014) Caffe: Convolutional architecture for fast feature embedding. [Online]. Available: http://arxiv.org/abs/1408.5093
Original
2014
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in Proc. Int. Conf. Learning Representations , 2015. [Online]. Available: http://arxiv.org/abs/1409.1556
Original
2015
Earlier work this paper cites.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proc. Comput. Vision and Pattern Recognition , 2015, pp. 1–9. [Online]. Available: http://arxiv.org/abs/1409.4842
Original
2015
Earlier work this paper cites.
R. Girshick, “Fast R-CNN,” in Proc. Int. Conf. Comput. Vision , 2015, pp. 1440–1448. [Online]. Available: http://arxiv.org/abs/1504.08083
Original
2015
Earlier work this paper cites.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” in Proc. Advances in Neural Inf. Process. Syst. , 2015, pp. 1–9
2015
Earlier work this paper cites.
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proc. Comput. Vision and Pattern Recognition , 2015. [Online]. Available: http://arxiv.org/abs/1411.4038
Original
2015
Earlier work this paper 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 Proc. ACM/SIGDA Int. Symp. Field Programmable Gate Arrays , 2015, pp. 161–170
2015
Earlier work this paper cites.
S. Han, J. Pool, J. Tran, and W. J. Dally, “Learning both weights and connections for efficient neural networks,” in Proc. Advances in Neural Inf. Process. Syst. , 2015, pp. 1135–1143. [Online]. Available: http://arxiv.org/abs/1506.02626
Original
2015
Earlier work this paper cites.

Similar

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, Z. Ma, S.a dn Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet large scale visual recognition challenge,” Int. J. Comput. Vision , vol. 115, no. 3, pp. 211–252, Dec. 2015
2015
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. Comput. Vision and Pattern Recognition , 2016, pp. 770–778. [Online]. Available: http://arxiv.org/abs/1512.03385
Original
2016
Cited alongside, same era.
F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, and K. Keutzer. (2016) SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and < < 0.5MB model size. [Online]. Available: http://arxiv.org/abs/1602.07360
Original
2016
Cited alongside, same era.
J. Albericio, P. Judd, T. Hetherington, T. Aamodt, N. E. Jerger, and A. Moshovos, “Cnvlutin: Ineffectual-neuron-free deep neural network computing,” in Proc. Int. Symp. Comput. Architecture , 2016, pp. 1–13
2016

Then

Y. He, X. Zhang, and J. Sun, “Channel pruning for accelerating very deep neural networks,” in Proc. Int. Conf. Comput. Vision , 2017, pp. 1398–1406. [Online]. Available: http://arxiv.org/abs/1707.06168
Original
2017
Later among the works it cites.
J. Yu, A. Lukefahr, D. Palframan, G. Dasika, R. Das, and S. Mahlke, “Scalpel: Customizing DNN pruning to the underlying hardware parallelism,” in Proc. Int. Symp. Comput. Architecture , 2017, pp. 548–560
2017
Later among the works it cites.
P. Molchanov, S. Tyree, T. Karras, T. Aila, and J. Kautz, “Pruning convolutional neural networks for resource efficient inference,” in Proc. Int. Conf. Learning Representations , 2017. [Online]. Available: http://arxiv.org/abs/1611.06440
Original
2017
Later among the works it cites.
N. Yu, S. Qiu, X. Hu, and J. Li, “Accelerating convolutional neural networks by group-wise 2D-filter pruning,” in Proc. Int. Joint Conf. Neural Networks , 2017, pp. 2502–2509
2017
Later among the works it cites.

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

Cited alongside, same era.

L. Song, Y. Wang, Y. Han, X. Zhao, B. Liu, and X. Li, “C-Brain: A deep learning accelerator that tames the diversity of CNNs through adaptive data-level parallelization,” in Proc. Design Automation Conf. , 2016, pp. 123:1–123:6

2016

Cited alongside, same era.

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 Proc. ACM/SIGDA Int. Symp. Field Programmable Gate Arrays , 2016, pp. 26–35

2016

Cited alongside, same era.

M. Motamedi, P. Gysel, V. Akella, and S. Ghiasi, “Design space exploration of FPGA-based deep convolutional neural networks,” in Proc. Asia and South Pacific Design Automation Conf. , 2016, pp. 575–580

2016

Cited alongside, same era.

S. Han, H. Mao, and W. J. Dally, “Deep Compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding,” in Proc. Int. Conf. Learning Representations , 2016. [Online]. Available: http://arxiv.org/abs/1510.00149

Original

2016

Cited alongside, same era.

V. Lebedev and V. Lempitsky, “Fast ConvNets using group-wise brain damage,” in Proc. Comput. Vision and Pattern Recognition , 2016, pp. 2554–2564. [Online]. Available: http://arxiv.org/abs/1506.02515

Original

2016

Cited alongside, same era.

D. Kadetotad, S. Arunachalam, C. Chakrabarti, and J.-S. Seo, “Efficient memory compression in deep neural networks using coarse-grain sparsification for speech applications,” in Proc. Int. Conf. Comput. Aided Design , 2016

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 Proc. Int. Symp. Comput. Architecture , 2016, pp. 243–254. [Online]. Available: http://arxiv.org/abs/1602.01528

Original

2016

Cited alongside, same era.

S. Zhang, Z. Du, L. Zhang, H. Lan, S. Liu, L. Li, Q. Guo, T. Chen, and Y. Chyen, “Cambricon-X: An accelerator for sparse neural networks,” in Proc. Int. Symp. Micro-architecture , 2016, pp. 20:1–20:12

2016

Cited alongside, same era.

S. Anwar, K. Hwang, and W. Sung, “Structured pruning of deep convolutional neural networks,” ACM J. on Emerging Technologies in Computing Syst. , vol. 13, no. 3, pp. 32:1–32:18, feb 2017

2017