Fetching the paper…
Reading the bibliography…
While neural networks have been remarkably successful in a wide array of applications, implementing them in resource-constrained hardware remains an area of intense research.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
An invitation to compressive sensing
S. Foucart and H. Rauhut · 2013
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Earlier work this paper cites.
Binaryconnect: Training deep neural networks with binary weights during propagations
M. Courbariaux, Y. Bengio, and J.-P. David · 2015
Earlier work this paper cites.
S. Han, H. Mao, and W. J. Dally · 2015
Earlier work this paper cites.
Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients
S. Zhou, Y. Wu, Z. Ni, X. Zhou, H. Wen, and Y. Zou · 2016
Earlier work this paper cites.
Incremental network quantization: Towards lossless cnns with low-precision weights
A. Zhou, A. Yao, Y. Guo, L. Xu, and Y. Chen · 2017
Earlier work this paper cites.
Post-training 4-bit quantization of convolution networks for rapid-deployment
R. Banner, Y. Nahshan, E. Hoffer, and D. Soudry · 2018
Earlier work this paper cites.
A survey on methods and theories of quantized neural networks
Y. Guo · 2018
Earlier work this paper cites.
Quantization and training of neural networks for efficient integer-arithmetic-only inference
B. Jacob, S. Kligys, B. Chen, M. Zhu, M. Tang, A. Howard, H. Adam, and D. Kalenichenko · 2018
Cited alongside, same era.
Quantizing deep convolutional networks for efficient inference: A whitepaper
R. Krishnamoorthi · 2018
Cited alongside, same era.
Quantization for rapid deployment of deep neural networks
J. H. Lee, S. Ha, S. Choi, W.-J. Lee, and S. Lee · 2018
Cited alongside, same era.
High-dimensional probability: An introduction with applications in data science , volume 47
R. Vershynin · 2018
Cited alongside, same era.
Lq-nets: Learned quantization for highly accurate and compact deep neural networks
D. Zhang, J. Yang, D. Ye, and G. Hua · 2018
Cited alongside, same era.
Improving neural network quantization without retraining using outlier channel splitting
R. Zhao, Y. Hu, J. Dotzel, C. De Sa, and Z. Zhang · 2019
Later among the works it cites.
Zeroq: A novel zero shot quantization framework
Y. Cai, Z. Yao, Z. Dong, A. Gholami, M. W. Mahoney, and K. Keutzer · 2020
Later among the works it cites.
Model compression and hardware acceleration for neural networks: A comprehensive survey
L. Deng, G. Li, S. Han, L. Shi, and Y. Xie · 2020
Later among the works it cites.
Post-training piecewise linear quantization for deep neural networks
J. Fang, A. Shafiee, H. Abdel-Aziz, D. Thorsley, G. Georgiadis, and J. H. Hassoun · 2020
Later among the works it cites.
Improving post training neural quantization: Layer-wise calibration and integer programming
I. Hubara, Y. Nahshan, Y. Hanani, R. Banner, and D. Soudry · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Low-bit quantization of neural networks for efficient inference
Y. Choukroun, E. Kravchik, F. Yang, and P. Kisilev · 2019
Cited alongside, same era.
Hawq: Hessian aware quantization of neural networks with mixed-precision
Z. Dong, Z. Yao, A. Gholami, M. W. Mahoney, and K. Keutzer · 2019
Cited alongside, same era.
Relaxed quantization for discretized neural networks
C. Louizos, M. Reisser, T. Blankevoort, E. Gavves, and M. Welling · 2019
Cited alongside, same era.
Data-free quantization through weight equalization and bias correction
M. Nagel, M. v. Baalen, T. Blankevoort, and M. Welling · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, et al · 2019
Cited alongside, same era.
Efficientnet: Rethinking model scaling for convolutional neural networks
M. Tan and Q. Le · 2019
Cited alongside, same era.
Towards accurate post-training network quantization via bit-split and stitching
P. Wang, Q. Chen, X. He, and J. Cheng
Cited in the paper.
Up or down? adaptive rounding for post-training quantization
M. Nagel, R. A. Amjad, M. Van Baalen, C. Louizos, and T. Blankevoort · 2020
Later among the works it cites.
Generative low-bitwidth data free quantization
S. Xu, H. Li, B. Zhuang, J. Liu, J. Cao, C. Liang, and M. Tan · 2020
Later among the works it cites.
A survey of quantization methods for efficient neural network inference
A. Gholami, S. Kim, Z. Dong, Z. Yao, M. W. Mahoney, and K. Keutzer · 2021
Later among the works it cites.
Brecq: Pushing the limit of post-training quantization by block reconstruction
Y. Li, R. Gong, X. Tan, Y. Yang, P. Hu, Q. Zhang, F. Yu, W. Wang, and S. Gu · 2021
Later among the works it cites.
Zero-shot adversarial quantization
Y. Liu, W. Zhang, and J. Wang · 2021
Later among the works it cites.
A greedy algorithm for quantizing neural networks
E. Lybrand and R. Saab · 2021
Later among the works it cites.