BMPQ: Bit-Gradient Sensitivity Driven Mixed-Precision Quantization of DNNs from Scratch

Y. Bengio, N. Léonard, and A. Courville, “Estimating or propagating gradients through stochastic neurons for conditional computation,” arXiv preprint arXiv:1308.3432 , 2013

Original

2013

Earlier work this paper cites.

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556 , 2014

Original

2014

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 , 2015

Original

2015

Earlier work this paper cites.

I. Hubara, M. Courbariaux, D. Soudry, R. El-Yaniv, and Y. Bengio, “Binarized neural networks,” Advances in neural information processing systems , vol. 29, 2016

2016

Earlier work this paper cites.

M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi, “XNOR-Net: Imagenet classification using binary convolutional neural networks,” in European conference on computer vision . Springer, 2016, pp. 525–542

2016

Earlier work this paper cites.

C. Zhu, S. Han, H. Mao, and W. J. Dally, “Trained ternary quantization,” arXiv preprint arXiv:1612.01064 , 2016

Original

2016

Earlier work this paper cites.

F. Li, B. Zhang, and B. Liu, “Ternary weight networks,” arXiv preprint arXiv:1605.04711 , 2016

Original

2016

Earlier work this paper cites.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778

2016

Earlier work this paper cites.

A. Inc., “An on-device deep neural network for face detection,” Nov 2017. [Online]. Available: https://machinelearning.apple.com/research/face-detection

2017

Earlier work this paper cites.

A. Zhou, A. Yao, Y. Guo, L. Xu, and Y. Chen, “Incremental network quantization: Towards lossless CNNs with low-precision weights,” arXiv preprint arXiv:1702.03044 , 2017

Original

2017

Cited alongside, same era.

B. Wu, Y. Wang, P. Zhang, Y. Tian, P. Vajda, and K. Keutzer, “Mixed precision quantization of ConvNets via differentiable neural architecture search,” arXiv preprint arXiv:1812.00090 , 2018

Original

2018

Cited alongside, same era.

D. Zhang, J. Yang, D. Ye, and G. Hua, “LQ-Nets: Learned quantization for highly accurate and compact deep neural networks,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 365–382

2018

Cited alongside, same era.

R. Krishnamoorthi, “Quantizing deep convolutional networks for efficient inference: A whitepaper,” arXiv preprint arXiv:1806.08342 , 2018

Original

2018

Cited alongside, same era.

A. S. Rakin, Z. He, and D. Fan, “Bit-flip attack: Crushing neural network with progressive bit search,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 1211–1220

2019

Later among the works it cites.

L. Deng, G. Li, S. Han, L. Shi, and Y. Xie, “Model compression and hardware acceleration for neural networks: A comprehensive survey,” Proceedings of the IEEE , vol. 108, no. 4, pp. 485–532, 2020

2020

Later among the works it cites.

S. Kundu, M. Nazemi, M. Pedram, K. M. Chugg, and P. A. Beerel, “Pre-defined sparsity for low-complexity convolutional neural networks,” IEEE Transactions on Computers , vol. 69, no. 7, pp. 1045–1058, 2020

2020

Later among the works it cites.

S. Kundu, M. Nazemi, P. A. Beerel, and M. Pedram, “DNR: A tunable robust pruning framework through dynamic network rewiring of DNNs,” in Proceedings of the 26th Asia and South Pacific Design Automation Conference , 2021, pp. 344–350

2021

Closest in time.