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Quantization of deep neural networks (DNN) has been proven effective for compressing and accelerating DNN models.
Hawq-v2: Hessian aware trace-weighted quantization of neural networks
Zhen Dong, Zhewei Yao, Yaohui Cai, Daiyaan Arfeen, Amir Gholami, Michael W Mahoney, and Kurt Keutzer · 1911
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Deep learning with limited numerical precision
Suyog Gupta, Ankur Agrawal, Kailash Gopalakrishnan, and Pritish Narayanan · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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Learning to prune deep neural networks via layer-wise optimal brain surgeon
Xin Dong, Shangyu Chen, and Sinno Jialin Pan · 2017
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Post-training 4-bit quantization of convolution networks for rapid-deployment
Ron Banner, Yury Nahshan, Elad Hoffer, and Daniel Soudry · 2018
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Squeezenext: Hardware-aware neural network design
Amir Gholami, Kiseok Kwon, Bichen Wu, Zizheng Tai, Xiangyu Yue, Peter Jin, Sicheng Zhao, and Kurt Keutzer · 2018
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Quantization and training of neural networks for efficient integer-arithmetic-only inference
Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, Hartwig Adam, and Dmitry Kalenichenko · 2018
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Shufflenet: An extremely efficient convolutional neural network for mobile devices
Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, and Jian Sun · 2018
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Low-bit quantization of neural networks for efficient inference
Yoni Choukroun, Eli Kravchik, Fan Yang, and Pavel Kisilev · 2019
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Sparten: A sparse tensor accelerator for convolutional neural networks
Ashish Gondimalla, Noah Chesnut, Mithuna Thottethodi, and TN Vijaykumar · 2019
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Data-free quantization through weight equalization and bias correction
Markus Nagel, Mart van Baalen, Tijmen Blankevoort, and Max Welling · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Adversarial defense through network profiling based path extraction
Yuxian Qiu, Jingwen Leng, Cong Guo, Quan Chen, Chao Li, Minyi Guo, and Yuhao Zhu · 2019
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Learning channel-wise interactions for binary convolutional neural networks
Ziwei Wang, Jiwen Lu, Chenxin Tao, Jie Zhou, and Qi Tian · 2019
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Improving neural network quantization without retraining using outlier channel splitting
Ritchie Zhao, Yuwei Hu, Jordan Dotzel, Chris De Sa, and Zhiru Zhang · 2019
Cited alongside, same era.
Zeroq: A novel zero shot quantization framework
Yaohui Cai, Zhewei Yao, Zhen Dong, Amir Gholami, Michael W Mahoney, and Kurt Keutzer · 2020
Cited alongside, same era.
Dissecting hessian: Understanding common structure of hessian in neural networks
Yikai Wu, Xingyu Zhu, Chenwei Wu, Annie Wang, and Rong Ge · 2020
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Generative low-bitwidth data free quantization
Shoukai Xu, Haokun Li, Bohan Zhuang, Jing Liu, Jiezhang Cao, Chuangrun Liang, and Mingkui Tan · 2020
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Sparch: Efficient architecture for sparse matrix multiplication
Zhekai Zhang, Hanrui Wang, Song Han, and William J Dally · 2020
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Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity
William Fedus, Barret Zoph, and Noam Shazeer · 2021
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Block-skim: Efficient question answering for transformer
Yue Guan, Zhengyi Li, Jingwen Leng, Zhouhan Lin, Minyi Guo, and Yuhao Zhu · 2021
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Yoojin Choi, Jihwan Choi, Mostafa El-Khamy, and Jungwon Lee · 2020
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Sparse gpu kernels for deep learning
Trevor Gale, Matei Zaharia, Cliff Young, and Erich Elsen · 2020
Cited alongside, same era.
How far does bert look at: Distance-based clustering and analysis of bert’s attention
Yue Guan, Jingwen Leng, Chao Li, Quan Chen, and Minyi Guo · 2020
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Balancing efficiency and flexibility for dnn acceleration via temporal gpu-systolic array integration
Cong Guo, Yangjie Zhou, Jingwen Leng, Yuhao Zhu, Zidong Du, Quan Chen, Chao Li, Bin Yao, and Minyi Guo · 2020
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Improving post training neural quantization: Layer-wise calibration and integer programming
Itay Hubara, Yury Nahshan, Yair Hanani, Ron Banner, and Daniel Soudry · 2020
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Up or down? adaptive rounding for post-training quantization
Markus Nagel, Rana Ali Amjad, Mart Van Baalen, Christos Louizos, and Tijmen Blankevoort · 2020
Cited alongside, same era.
Channel-wise hessian aware trace-weighted quantization of neural networks
Xu Qian, Victor Li, and Crews Darren · 2020
Cited alongside, same era.
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Brecq: Pushing the limit of post-training quantization by block reconstruction
Yuhang Li, Ruihao Gong, Xu Tan, Yang Yang, Peng Hu, Qi Zhang, Fengwei Yu, Wei Wang, and Shi Gu · 2021
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Zero-shot adversarial quantization
Yuang Liu, Wei Zhang, and Jun Wang · 2021
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Diverse sample generation: Pushing the limit of data-free quantization
Haotong Qin, Yifu Ding, Xiangguo Zhang, Aoyu Li, Jiakai Wang, Xianglong Liu, and Jiwen Lu · 2021
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Dual-side sparse tensor core
Yang Wang, Chen Zhang, Zhiqiang Xie, Cong Guo, Yunxin Liu, and Jingwen Leng · 2021
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Hawq-v3: Dyadic neural network quantization
Zhewei Yao, Zhen Dong, Zhangcheng Zheng, Amir Gholami, Jiali Yu, Eric Tan, Leyuan Wang, Qijing Huang, Yida Wang, Michael Mahoney, et al · 2021
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Hessian-aware pruning and optimal neural implant
Shixing Yu, Zhewei Yao, Amir Gholami, Zhen Dong, Michael W Mahoney, and Kurt Keutzer · 2021
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Diversifying sample generation for accurate data-free quantization
Xiangguo Zhang, Haotong Qin, Yifu Ding, Ruihao Gong, Qinghua Yan, Renshuai Tao, Yuhang Li, Fengwei Yu, and Xianglong Liu · 2021
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Effective training of convolutional neural networks with low-bitwidth weights and activations
Bohan Zhuang, Mingkui Tan, Jing Liu, Lingqiao Liu, Ian Reid, and Chunhua Shen · 2021
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