Minimum Energy Quantized Neural Networks

Gradient-based learning applied to document recognition

Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998

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Alex Krizhevsky and Geoffrey Hinton · 2009

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Reading digits in natural images with unsupervised feature learning

Original

Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011

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Yoshua Bengio, Nicholas Léonard, and Aaron Courville · 2013

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Draining our glass: An energy and heat characterization of google glass

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Very deep convolutional networks for large-scale image recognition

Karen Simonyan and Andrew Zisserman · 2014

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Deep learning

Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015

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Eyeriss: An energy-efficient reconfigurable accelerator for deep convolutional neural networks

Yu-Hsin Chen, Tushar Krishna, Joel S Emer, and Vivienne Sze · 2016

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Eie: efficient inference engine on compressed deep neural network

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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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Trained ternary quantization

Chenzhuo Zhu, Song Han, Huizi Mao, and William J Dally · 2016

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Energy-efficient convnets through approximate computing

Bert Moons, Bert De Brabandere, Luc Van Gool, and Marian Verhelst · 2016

Quantized neural networks: Training neural networks with low precision weights and activations

Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 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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Envision: A 0.26-to-10 tops/w subword-parallel dynamic-voltage-accuracy-frequency-scalable convolutional neural network processor in 28nm fdsoi

Bert Moons, Roel Uytterhoeven, Wim Dehaene, and Marian Verhelst · 2017

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A novel zero weight/activation-aware hardware architecture of convolutional neural network

Dongyoung Kim, Junwhan Ahn, and Sungjoo Yoo · 2017

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github.com/aaron-xichen/pytorch-playground

Tested model quantization · 2017

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