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Optimization of Top-1 ImageNet promotes enormous networks that may be impractical in inference settings.
A suggestion for a fast multiplier
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Systolic arrays (for vlsi)
HT Kung and Charles E Leiserson · 1979
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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1.1 computing’s energy problem (and what we can do about it)
Mark Horowitz · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Zhiyong Cheng, Daniel Soudry, Zexi Mao, and Zhenzhong Lan · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 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
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 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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Binarized neural networks
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 2016
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Minje Kim and Paris Smaragdis · 2016
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XNOR-Net: ImageNet classification using binary convolutional neural networks
Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi · 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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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
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Training quantized nets: A deeper understanding
Hao Li, Soham De, Zheng Xu, Christoph Studer, Hanan Samet, and Tom Goldstein · 2017
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Learning accurate low-bit deep neural networks with stochastic quantization
Jianguo Li Yinpeng Dong and Renkun Ni · 2017
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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
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Jungwook Choi, Zhuo Wang, Swagath Venkataramani, Pierce I-Jen Chuang, Vijayalakshmi Srinivasan, and Kailash Gopalakrishnan · 2018
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FBNA: A fully binarized neural network accelerator
Peng Guo, Hong Ma, Ruizhi Chen, Pin Li, Shaolin Xie, and Donglin Wang · 2018
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ReActNet: Towards precise binary neural network with generalized activation functions
Zechun Liu, Zhiqiang Shen, Marios Savvides, and Kwang-Ting Cheng · 2020
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Dprelu: Dynamic parametric rectified linear unit
Kien Mai Ngoc, Donghun Yang, Iksoo Shin, Hoyong Kim, and Myunggwon Hwang · 2020
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Training binary neural networks with real-to-binary convolutions
Brais Martinez, Jing Yang, Adrian Bulat, and Georgios Tzimiropoulos · 2020
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NVIDIA A100 Tensor Core GPU Architecture, 2020
NVIDIA · 2020
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MoBiNet: A mobile binary network for image classification
Hai Phan, Dang The Huynh, Yihui He, Marios Savvides, and Zhiqiang Shen · 2020
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Binarizing mobilenet via evolution-based searching
Hai Phan, Zechun Liu, Dang Huynh, Marios Savvides, Kwang-Ting Cheng, and Zhiqiang Shen · 2020
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Squeeze-and-excitation networks
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Mobilenetv2: Inverted residuals and linear bottlenecks
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HadaNets: Flexible quantization strategies for neural networks
Yash Akhauri · 2019
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Xnor-net++: Improved binary neural networks
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Improved training of binary networks for human pose estimation and image recognition
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Forward and backward information retention for accurate binary neural networks
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Pareto-optimal quantized resnet is mostly 4-bit
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A comprehensive review of binary neural network
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FracBNN: Accurate and FPGA-efficient binary neural networks with fractional activations
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