Fetching the paper…
Reading the bibliography…
Quantization has become a popular technique to compress neural networks and reduce compute cost, but most prior work focuses on studying quantization without changing the network size.
A three-dimensional approach to parallel matrix multiplication
Ramesh C Agarwal, Susanne M Balle, Fred G Gustavson, Mahesh Joshi, and Prasad Palkar · 1995
Earlier work this paper cites.
Convolutional deep belief networks on cifar-10, 2010
Alex Krizhevsky · 2010
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
Earlier work this paper cites.
Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding, 2016
Song Han, Huizi Mao, and William J. Dally · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le · 2016
Earlier work this paper cites.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
Earlier work this paper cites.
Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Earlier work this paper cites.
Inception-v4, inception-resnet and the impact of residual connections on learning
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander Alemi · 2017
Earlier work this paper cites.
Nice: Noise injection and clamping estimation for neural network quantization
Chaim Baskin, Natan Liss, Yoav Chai, Evgenii Zheltonozhskii, Eli Schwartz, Raja Giryes, Avi Mendelson, and Alexander M Bronstein · 2018
Earlier work this paper cites.
JAX: composable transformations of Python+NumPy programs
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang · 2018
Earlier work this paper cites.
Pact: Parameterized clipping activation for quantized neural networks
Jungwook Choi, Zhuo Wang, Swagath Venkataramani, Pierce I-Jen Chuang, Vijayalakshmi Srinivasan, and Kailash Gopalakrishnan · 2018
Earlier work this paper cites.
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
Earlier work this paper cites.
Auto-tuning neural network quantization framework for collaborative inference between the cloud and edge
Guangli Li, Lei Liu, Xueying Wang, Xiao Dong, Peng Zhao, and Xiaobing Feng · 2018
Cited alongside, same era.
Discovering low-precision networks close to full-precision networks for efficient embedded inference
Jeffrey L McKinstry, Steven K Esser, Rathinakumar Appuswamy, Deepika Bablani, John V Arthur, Izzet B Yildiz, and Dharmendra S Modha · 2018
Cited alongside, same era.
Lq-nets: Learned quantization for highly accurate and compact deep neural networks
Dongqing Zhang, Jiaolong Yang, Dongqiangzi Ye, and Gang Hua · 2018
Cited alongside, same era.
Towards effective low-bitwidth convolutional neural networks
Bohan Zhuang, Chunhua Shen, Mingkui Tan, Lingqiao Liu, and Ian Reid · 2018
Cited alongside, same era.
Hawq: Hessian aware quantization of neural networks with mixed-precision
Zhen Dong, Zhewei Yao, Amir Gholami, Michael W Mahoney, and Kurt Keutzer · 2019
Cited alongside, same era.
Post-training piecewise linear quantization for deep neural networks
Jun Fang, Ali Shafiee, Hamzah Abdel-Aziz, David Thorsley, Georgios Georgiadis, and Joseph H Hassoun · 2020
Later among the works it cites.
Flax: A neural network library and ecosystem for JAX
Jonathan Heek, Anselm Levskaya, Avital Oliver, Marvin Ritter, Bertrand Rondepierre, Andreas Steiner, and Marc van Zee · 2020
Later among the works it cites.
Spatial shift point-wise quantization
Eunhui Kim and Kyong-Ha Lee · 2020
Later among the works it cites.
Weight equalizing shift scaler-coupled post-training quantization
Jihun Oh, SangJeong Lee, Meejeong Park, Pooni Walagaurav, and Kiseok Kwon · 2020
Later among the works it cites.
Robust quantization: One model to rule them all
Moran Shkolnik, Brian Chmiel, Ron Banner, Gil Shomron, Yuri Nahshan, Alex Bronstein, and Uri Weiser · 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…
Neural architecture search: A survey
Thomas Elsken, Jan Hendrik Metzen, Frank Hutter, et al · 2019
Cited alongside, same era.
Sambhav R Jain, Albert Gural, Michael Wu, and Chris H Dick · 2019
Cited alongside, same era.
Regularized evolution for image classifier architecture search
Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V Le · 2019
Cited alongside, same era.
Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 2019
Cited alongside, same era.
Haq: Hardware-aware automated quantization with mixed precision
Kuan Wang, Zhijian Liu, Yujun Lin, Ji Lin, and Song Han · 2019
Cited alongside, same era.
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.
Integer quantization for deep learning inference: Principles and empirical evaluation
Hao Wu, Patrick Judd, Xiaojie Zhang, Mikhail Isaev, and Paulius Micikevicius · 2020
Later among the works it cites.
Vs-quant: Per-vector scaled quantization for accurate low-precision neural network inference
Steve Dai, Rangharajan Venkatesan, Haoxing Ren, Brian Zimmer, William J Dally, and Brucek Khailany · 2021
Closest in time.
Confounding tradeoffs for neural network quantization
Sahaj Garg, Anirudh Jain, Joe Lou, and Mitchell Nahmias · 2021
Closest in time.
Nvidia a100 tensor core gpu architecture
NVIDIA · 2021
Closest in time.
Effective and fast: A novel sequential single path search for mixed-precision quantization
Qigong Sun, Licheng Jiao, Yan Ren, Xiufang Li, Fanhua Shang, and Fang Liu · 2021
Closest in time.
Convolutional neural network with int4 optimization on xilinx devices
Xilinx · 2021
Closest in time.
Fracbnn: Accurate and fpga-efficient binary neural networks with fractional activations
Yichi Zhang, Junhao Pan, Xinheng Liu, Hongzheng Chen, Deming Chen, and Zhiru Zhang · 2021
Closest in time.