Citation: | YUAN Yong, CHEN Chen, HU Xiyuan, et al., “CNQ: Compressor-Based Non-uniform Quantization of Deep Neural Networks,” Chinese Journal of Electronics, vol. 29, no. 6, pp. 1126-1133, 2020, doi: 10.1049/cje.2020.09.014 |
Y. Liu, H. Liu, J. Fan, et al., "A survey of research and application of small object detection based on deep learning", Chinese Journal of Electronics, Vol.48, No.3, pp.590-601, 2020.
|
M. Courbariaux, Y. Bengio and J.P. David, "BinaryConnect:Training deep neural networks with binary weights during propagations", Proceedings of the International Conference on Neural Information Processing Systems, Lille, France, pp.3123-3131, 2015.
|
I. Hubara, D. Soudry and R.E Yaniv, "Binarized neural networks", Advances in Neural Information Processing Systems, Barcelona, Spain, pp.4107-4115, 2016.
|
M. Rastegari, V. Ordonez and J. Redmon, "XNOR-Net:ImageNet classification using binary convolutional neural networks", Proceedings of the European Conference on Computer Vision, Amsterdam, Netherlands, pp.525-542, 2016.
|
S. Zhou, Y. Wu, Z. Ni, et al., "Dorefa-net:Training low bitwidth convolutional neural networks with low bitwidth gradients", arXiv preprint, arXiv:1606.06160, 2016.
|
Q. Jian, P. Zhang and X. Wang, "An FPGA implementation method for configurable CNN co-accelerator", Chinese Journal of Electronics, Vol.47, No.7, pp.1525-1531, 2019.
|
S. Han, H. Mao and W.J. Dally, "Deep compression:Compressing deep neural networks with pruning, trained quantization and huffman coding", Proceedings of the International Conference on Learning Representations, 2016.
|
E. L. Denton, W. Zaremba, J. Bruna, et al., "Exploiting linear structure within convolutional networks for efficient evaluation", Advances in Neural Information Processing Systems, Montreal, QC, Canada, pp.1269-1277, 2014.
|
A.G. Howard, M. Zhu, B.Chen, et al., "Mobilenets:Efficient convolutional neural networks for mobile vision applications", arXiv preprint, arXiv:1704.04861, 2017.
|
G. Hinton, O. Vinyals and J. Dean, "Distilling the knowledge in a neural network", arXiv preprint, arXiv:1503.02531, 2015.
|
A. Romero, N. Ballas, S.E. Kahou, et al., "Fitnets:Hints for thin deep nets", International Conference on Learning Representations, 2015.
|
A. Howard, M. Sandler, G. Chu, et al., "Searching for mobilenetv3", Proceedings of the IEEE International Conference on Computer Vision, pp.1314-1324, 2019.
|
S. Migacz, "8-bit inference with TensorRT", GPU Technology Conference, San Jose, CA, USA, Page 7, 2017.
|
B. Ron, N. Yury, H. Elad, et al., "Post training 4-bit quantization of convolution networks for rapid-deployment", Advances in Neural Information Processing Systems, Vancouver, Canada, pp.7948-7956, 2019.
|
X. He and J. Cheng, "Learning compression from limited unlabeled data", Proceedings of the European Conference on Computer Vision, Munich, Germany, pp.752-769, 2018.
|
Y. Choukroun, E. Kravchik and P. Kisilev, "Low-bit quantization of neural networks for efficient inference", Proceedings of the IEEE International Conference on Computer Vision Workshops, 2019.
|
E. Park, J. Ahn and S. Yoo, "Weighted-entropy-based quantization for deep neural networks", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, Hawaii, USA, pp.5456-5464, 2017.
|
Y. Wei, X. Pan, H. Qin, et al., "Quantization mimic:Towards very tiny cnn for object detection", Proceedings of the European Conference on Computer Vision, Munich, Germany, pp.267-283, 2018.
|
B. Zhuang, C. Shen, M. Tan, et al., "Towards effective low-bitwidth convolutional neural networks", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, Utah, USA, pp.7920-7928, 2018.
|
N.S. Jayant and P. Noll, "Digital coding of waveforms:Principles and applications to speech and video", Englewood Cliffs, NJ, pp.115-251, 1984.
|
N. Judell and L. Scharf, "A simple derivation of Lloyd's classical result for the optimum scalar quantizer", IEEE Transactions on Information Theory, Vol.32, No.2, pp.326-328, 1986.
|
Y. Yuan, C. Chen, X. Hu, et al., "Unlabeled data driven channel-wise bit-width allocation and quantization refinement", International Conference on Neural Information Processing, Sydney, Australia, pp.9-16, 2019.
|
W. Liu, D.Anguelov, D. Erhan, et al., "SSD:Single shot multibox detector", Proceedings of the European Conference on Computer Vision, Amsterdam, Netherlands, pp.21-37, 2016.
|
S. Liu and D. Huang, "Receptive field block net for accurate and fast object detection", Proceedings of the European Conference on Computer Vision, Munich, Germany, pp.385-400, 2018.
|