Fetching the paper…
Reading the bibliography…
Low-resolution neural networks represent both weights and activations with few bits, drastically reducing the multiplication complexity.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Binaryconnect: Training deep neural networks with binary weights during propagations
Matthieu Courbariaux, Yoshua Bengio, and Jean-Pierre David · 2015
Earlier work this paper cites.
https://github.com/google/gemmlowp
Google gemmlowp, 2016 · 2016
Earlier work this paper cites.
Matthieu Courbariaux, Itay Hubara, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 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.
Binarized neural networks
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 2016
Earlier work this paper cites.
Fengfu Li, Bo Zhang, and Bin Liu · 2016
Earlier work this paper cites.
Xnor-net: Imagenet classification using binary convolutional neural networks
Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi · 2016
Earlier work this paper cites.
Imagenet pre-trained models with batch normalization
Marcel Simon, Erik Rodner, and Joachim Denzler · 2016
Earlier work this paper cites.
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
Earlier work this paper cites.
Chenzhuo Zhu, Song Han, Huizi Mao, and William J Dally · 2016
Cited alongside, same era.
Learning accurate low-bit deep neural networks with stochastic quantization
Yinpeng Dong, Renkun Ni, Jianguo Li, Yurong Chen, Jun Zhu, and Hang Su · 2017
Cited alongside, same era.
Training quantized nets: A deeper understanding
Hao Li, Soham De, Zheng Xu, Christoph Studer, Hanan Samet, and Tom Goldstein · 2017
Cited alongside, same era.
Towards accurate binary convolutional neural network
Xiaofan Lin, Cong Zhao, and Wei Pan · 2017
Cited alongside, same era.
Paulius Micikevicius, Sharan Narang, Jonah Alben, Gregory Diamos, Erich Elsen, David Garcia, Boris Ginsburg, Michael Houston, Oleksii Kuchaiev, Ganesh Venkatesh, et al · 2017
Cited alongside, same era.
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
Later among the works it cites.
Espresso: Efficient forward propagation for binary deep neural networks
Fabrizio Pedersoli, George Tzanetakis, and Andrea Tagliasacchi · 2018
Later among the works it cites.
Training deep neural networks with 8-bit floating point numbers
Naigang Wang, Jungwook Choi, Daniel Brand, Chia-Yu Chen, and Kailash Gopalakrishnan · 2018
Later among the works it cites.
Adaptive layerwise quantization for deep neural network compression
Xiaotian Zhu, Wengang Zhou, and Houqiang Li · 2018
Later among the works it cites.
Xnor-net++: Improved binary neural networks
Adrian Bulat and Georgios Tzimiropoulos · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Apprentice: Using knowledge distillation techniques to improve low-precision network accuracy
Asit Mishra and Debbie Marr · 2017
Cited alongside, same era.
Wrpn: wide reduced-precision networks
Asit Mishra, Eriko Nurvitadhi, Jeffrey J Cook, and Debbie Marr · 2017
Cited alongside, same era.
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 · 2018
Cited alongside, same era.
Pact: Parameterized clipping activation for quantized neural networks
Jungwook Choi, Zhuo Wang, Swagath Venkataramani, Pierce I-Jen Chuang, Vijayalakshmi Srinivasan, and Kailash Gopalakrishnan · 2018
Cited alongside, same era.
Fbgemm, 2018
Jongsoo Park et al · 2018
Cited alongside, same era.
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
Later among the works it cites.
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
Later among the works it cites.
Accumulation bit-width scaling for ultra-low precision training of deep networks
Charbel Sakr, Naigang Wang, Chia-Yu Chen, Jungwook Choi, Ankur Agrawal, Naresh Shanbhag, and Kailash Gopalakrishnan · 2019
Later among the works it cites.
Haq: Hardware-aware automated quantization with mixed precision
Kuan Wang, Zhijian Liu, Yujun Lin, Ji Lin, and Song Han · 2019
Later among the works it cites.
Quantization of deep neural networks for accumulator-constrained processors
Barry de Bruin, Zoran Zivkovic, and Henk Corporaal · 2020
Closest in time.