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Quantization is an effective technique to reduce memory footprint, inference latency, and power consumption of deep learning models.
Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al. 2019 · 1910
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Sharpness-aware minimization for efficiently improving generalization
Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur. 2020 · 2010
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Binarybert: Pushing the limit of bert quantization
Haoli Bai, Wei Zhang, Lu Hou, Lifeng Shang, Jing Jin, Xin Jiang, Qun Liu, Michael Lyu, and Irwin King. 2020 · 2012
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Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron Courville. 2013 · 2013
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On large-batch training for deep learning: Generalization gap and sharp minima
Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang. 2016 · 2016
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 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 · 2018
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Glue: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman. 2018 · 2018
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A comparative study on transformer vs rnn in speech applications
Shigeki Karita, Nanxin Chen, Tomoki Hayashi, Takaaki Hori, Hirofumi Inaguma, Ziyan Jiang, Masao Someki, Nelson Enrique Yalta Soplin, Ryuichi Yamamoto, Xiaofei Wang, et al. 2019 · 2019
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Q8bert: Quantized 8bit bert
Ofir Zafrir, Guy Boudoukh, Peter Izsak, and Moshe Wasserblat. 2019 · 2019
Cited alongside, same era.
Quantization aware training with tensorflow model optimization toolkit - performance with accuracy
Pulkit Bhuwalka, Alan Chiao, Suharsh Sivakumar, Raziel Alvarez, Feng Liu, Lawrence Chan, Skirmantas Kligys, Yunlu Li, Khanh LeViet, Billy Lambert, Mark Daoust, Tim Davis, Sarah Sirajuddin, and François Chollet. 2020 · 2020
Cited alongside, same era.
The lottery ticket hypothesis for pre-trained bert networks
Tianlong Chen, Jonathan Frankle, Shiyu Chang, Sijia Liu, Yang Zhang, Zhangyang Wang, and Michael Carbin. 2020 · 2020
Cited alongside, same era.
Learned step size quantization
Steven K Esser, Jeffrey L McKinstry, Deepika Bablani, Rathinakumar Appuswamy, and Dharmendra S Modha. 2020 · 2020
Cited alongside, same era.
Trained quantization thresholds for accurate and efficient fixed-point inference of deep neural networks
Sambhav Jain, Albert Gural, Michael Wu, and Chris Dick. 2020 · 2020
Cited alongside, same era.
Gobo: Quantizing attention-based nlp models for low latency and energy efficient inference
Ali Hadi Zadeh, Isak Edo, Omar Mohamed Awad, and Andreas Moshovos. 2020 · 2020
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When vision transformers outperform resnets without pre-training or strong data augmentations
Xiangning Chen, Cho-Jui Hsieh, and Boqing Gong. 2021 · 2021
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A survey of quantization methods for efficient neural network inference
Amir Gholami, Sehoon Kim, Zhen Dong, Zhewei Yao, Michael W Mahoney, and Kurt Keutzer. 2021 · 2021
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I-bert: Integer-only bert quantization
Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W Mahoney, and Kurt Keutzer. 2021 · 2021
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An empirical investigation of the role of pre-training in lifelong learning
Sanket Vaibhav Mehta, Darshan Patil, Sarath Chandar, and Emma Strubell. 2021 · 2021
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Yury Nahshan, Brian Chmiel, Chaim Baskin, Evgenii Zheltonozhskii, Ron Banner, Alex M. Bronstein, and Avi Mendelson. 2020 · 2020
Cited alongside, same era.
Q-bert: Hessian based ultra low precision quantization of bert
Sheng Shen, Zhen Dong, Jiayu Ye, Linjian Ma, Zhewei Yao, Amir Gholami, Michael W Mahoney, and Kurt Keutzer. 2020 · 2020
Cited alongside, same era.
Mixed precision dnns: All you need is a good parametrization
Stefan Uhlich, Lukas Mauch, Fabien Cardinaux, Kazuki Yoshiyama, Javier Alonso Garcia, Stephen Tiedemann, Thomas Kemp, and Akira Nakamura. 2020 · 2020
Cited alongside, same era.
Later among the works it cites.
Learnable companding quantization for accurate low-bit neural networks
Kohei Yamamoto. 2021 · 2021
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Ternarybert: Distillation-aware ultra-low bit bert
Wei Zhang, Lu Hou, Yichun Yin, Lifeng Shang, Xiao Chen, Xin Jiang, and Qun Liu. 2020 · 2021
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Sharpness-aware quantization for deep neural networks
Jing Liu, Jianfei Cai, and Bohan Zhuang. 2022 · 2022
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