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We propose Additive Powers-of-Two~(APoT) quantization, an efficient non-uniform quantization scheme for the bell-shaped and long-tailed distribution of weights and activations in neural networks.
What every computer scientist should know about floating-point arithmetic
David Goldberg · 1991
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Estimating or propagating gradients through stochastic neurons for conditional computation
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Predicting parameters in deep learning
Misha Denil, Babak Shakibi, Laurent Dinh, Marc’Aurelio Ranzato, and Nando De Freitas · 2013
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Song Han, Huizi Mao, and William J Dally · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Imagenet large scale visual recognition challenge
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Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Convolutional neural networks using logarithmic data representation
Daisuke Miyashita, Edward H Lee, and Boris Murmann · 2016
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Weight normalization: A simple reparameterization to accelerate training of deep neural networks
Tim Salimans and Durk P Kingma · 2016
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Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients
Shuchang Zhou, Yuxin Wu, Zekun Ni, Xinyu Zhou, He Wen, and Yuheng Zou · 2016
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Chenzhuo Zhu, Song Han, Huizi Mao, and William J Dally · 2016
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Deep learning with low precision by half-wave gaussian quantization
Zhaowei Cai, Xiaodong He, Jian Sun, and Nuno Vasconcelos · 2017
Model compression via distillation and quantization
Antonio Polino, Razvan Pascanu, and Dan Alistarh · 2018
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Lq-nets: Learned quantization for highly accurate and compact deep neural networks
Dongqing Zhang, Jiaolong Yang, Dongqiangzi Ye, and Gang Hua · 2018
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Weight normalization based quantization for deep neural network compression
Wen-Pu Cai and Wu-Jun Li · 2019
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Differentiable soft quantization: Bridging full-precision and low-bit neural networks
Ruihao Gong, Xianglong Liu, Shenghu Jiang, Tianxiang Li, Peng Hu, Jiazhen Lin, Fengwei Yu, and Junjie Yan · 2019
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Learning to quantize deep networks by optimizing quantization intervals with task loss
Sangil Jung, Changyong Son, Seohyung Lee, Jinwoo Son, Jae-Joon Han, Youngjun Kwak, Sung Ju Hwang, and Changkyu Choi · 2019
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Towards accurate binary convolutional neural network
Xiaofan Lin, Cong Zhao, and Wei Pan · 2017
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Incremental network quantization: Towards lossless cnns with low-precision weights
Aojun Zhou, Anbang Yao, Yiwen Guo, Lin Xu, and Yurong Chen · 2017
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Norm matters: efficient and accurate normalization schemes in deep networks
Elad Hoffer, Ron Banner, Itay Golan, and Daniel Soudry · 2018
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Bridging the accuracy gap for 2-bit quantized neural networks (qnn)
Jungwook Choi, Pierce I-Jen Chuang, Zhuo Wang, Swagath Venkataramani, Vijayalakshmi Srinivasan, and Kailash Gopalakrishnan
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Pact: Parameterized clipping activation for quantized neural networks
Jungwook Choi, Zhuo Wang, Swagath Venkataramani, Pierce I-Jen Chuang, Vijayalakshmi Srinivasan, and Kailash Gopalakrishnan
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Siyuan Qiao, Huiyu Wang, Chenxi Liu, Wei Shen, and Alan Yuille · 2019
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Improving neural network quantization without retraining using outlier channel splitting
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Learned step size quantization
Steven K. Esser, Jeffrey L. McKinstry, Deepika Bablani, Rathinakumar Appuswamy, and Dharmendra S. Modha · 2020
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