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We consider the post-training quantization problem, which discretizes the weights of pre-trained deep neural networks without re-training the model.
Low-bit quantization of neural networks for efficient inference
Choukroun, Y.; Kravchik, E.; and Kisilev, P. 2019 · 1902
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Same, same but different-recovering neural network quantization error through weight factorization
Meller, E.; Finkelstein, A.; Almog, U.; and Grobman, M. 2019 · 1902
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Data-Free Quantization through Weight Equalization and Bias Correction
Nagel, M.; van Baalen, M.; Blankevoort, T.; and Welling, M. 2019 · 1906
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Additive Powers-of-Two Quantization: A Non-uniform Discretization for Neural Networks
Li, Y.; Dong, X.; and Wang, W. 2019 · 1909
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Loss Aware Post-training Quantization
Nahshan, Y.; Chmiel, B.; Baskin, C.; Zheltonozhskii, E.; Banner, R.; Bronstein, A. M.; and Mendelson, A. 2019 · 1911
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The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results
Everingham, M.; Van Gool, L.; Williams, C. K. I.; Winn, J.; and Zisserman, A. ???? · 2007
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Imagenet classification with deep convolutional neural networks
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1.1 computing’s energy problem (and what we can do about it)
Horowitz, M. 2014 · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K.; and Zisserman, A. 2014 · 2014
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Binaryconnect: Training deep neural networks with binary weights during propagations
Courbariaux, M.; Bengio, Y.; and David, J.-P. 2015 · 2015
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Han, S.; Mao, H.; and Dally, W. J. 2015 · 2015
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Resiliency of deep neural networks under quantization
Sung, W.; Shin, S.; and Hwang, K. 2015 · 2015
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Going deeper with convolutions
Szegedy, C.; Liu, W.; Jia, Y.; Sermanet, P.; Reed, S.; Anguelov, D.; Erhan, D.; Vanhoucke, V.; and Rabinovich, A. 2015 · 2015
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Eyeriss: An energy-efficient reconfigurable accelerator for deep convolutional neural networks
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Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Xnor-net: Imagenet classification using binary convolutional neural networks
Rastegari, M.; Ordonez, V.; Redmon, J.; and Farhadi, A. 2016 · 2016
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Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients
Zhou, S.; Wu, Y.; Ni, Z.; Zhou, X.; Wen, H.; and Zou, Y. 2016 · 2016
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Zhu, C.; Han, S.; Mao, H.; and Dally, W. J. 2016 · 2016
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Quantizing deep convolutional networks for efficient inference: A whitepaper
Krishnamoorthi, R. 2018 · 2018
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Relaxed quantization for discretized neural networks
Louizos, C.; Reisser, M.; Blankevoort, T.; Gavves, E.; and Welling, M. 2018 · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
Sandler, M.; Howard, A.; Zhu, M.; Zhmoginov, A.; and Chen, L.-C. 2018 · 2018
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Scaling for edge inference of deep neural networks
Xu, X.; Ding, Y.; Hu, S. X.; Niemier, M.; Cong, J.; Hu, Y.; and Shi, Y. 2018 · 2018
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Neural Network Distiller
Zmora, N.; Jacob, G.; Zlotnik, L.; Elharar, B.; and Novik, G. 2018 · 2018
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Network sketching: Exploiting binary structure in deep cnns
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
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Quantized neural networks: Training neural networks with low precision weights and activations
Hubara, I.; Courbariaux, M.; Soudry, D.; El-Yaniv, R.; and Bengio, Y. 2017 · 2017
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Towards accurate binary convolutional neural network
Lin, X.; Zhao, C.; and Pan, W. 2017 · 2017
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8-bit inference with tensorrt
Migacz, S. 2017 · 2017
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WRPN: wide reduced-precision networks
Mishra, A.; Nurvitadhi, E.; Cook, J. J.; and Marr, D. 2017 · 2017
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Recent Advances in Efficient Computation of Deep Convolutional Neural Networks
Cheng, J.; Wang, P.; Li, G.; Hu, Q.; and Lu, H. 2018 · 2018
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Post training 4-bit quantization of convolutional networks for rapid-deployment
Banner, R.; Nahshan, Y.; and Soudry, D. 2019 · 2019
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HAWQ: Hessian AWare Quantization of Neural Networks With Mixed-Precision
Dong, Z.; Yao, Z.; Gholami, A.; Mahoney, M. W.; and Keutzer, K. 2019 · 2019
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Mixed Precision Neural Architecture Search for Energy Efficient Deep Learning
Gong, C.; Jiang, Z.; Wang, D.; Lin, Y.; Liu, Q.; and Pan, D. Z. 2019 · 2019
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Learning to quantize deep networks by optimizing quantization intervals with task loss
Jung, S.; Son, C.; Lee, S.; Son, J.; Han, J.-J.; Kwak, Y.; Hwang, S. J.; and Choi, C. 2019 · 2019
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HAQ: Hardware-Aware Automated Quantization with Mixed Precision
Wang, K.; Liu, Z.; Lin, Y.; Lin, J.; and Han, S. 2019 · 2019
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Improving Neural Network Quantization without Retraining using Outlier Channel Splitting
Zhao, R.; Hu, Y.; Dotzel, J.; De Sa, C.; and Zhang, Z. 2019 · 2019
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Structured binary neural networks for accurate image classification and semantic segmentation
Zhuang, B.; Shen, C.; Tan, M.; Liu, L.; and Reid, I. 2019 · 2019
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