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Binarized Neural Networks (BNNs) can significantly reduce the inference latency and energy consumption in resource-constrained devices due to their pure-logical computation and fewer memory accesses.
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STMicroelectronics · 2008
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Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
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Estimating or propagating gradients through stochastic neurons for conditional computation
Y. Bengio, N. Léonard, and A. Courville · 2013
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Rectifier nonlinearities improve neural network acoustic models
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On the importance of initialization and momentum in deep learning
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Adam: A method for stochastic optimization
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The loss surfaces of multilayer networks
A. Choromanska, M. Henaff, M. Mathieu, G. B. Arous, and Y. LeCun · 2015
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Binaryconnect: Training deep neural networks with binary weights during propagations
M. Courbariaux, Y. Bengio, and J.-P. David · 2015
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Deep learning with limited numerical precision
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
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Imagenet large scale visual recognition challenge
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Identity mappings in deep residual networks
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Binarized neural networks
I. Hubara, M. Courbariaux, D. Soudry, R. El-Yaniv, and Y. Bengio · 2016
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An empirical analysis of deep network loss surfaces
D. J. Im, M. Tao, and K. Branson · 2016
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Deep learning without poor local minima
K. Kawaguchi · 2016
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Fixed point quantization of deep convolutional networks
D. Lin, S. Talathi, and S. Annapureddy · 2016
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Xnor-net: Imagenet classification using binary convolutional neural networks
M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi · 2016
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Weight normalization: A simple reparameterization to accelerate training of deep neural networks
T. Salimans and D. P. Kingma · 2016
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No bad local minima: Data independent training error guarantees for multilayer neural networks
D. Soudry and Y. Carmon · 2016
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Exploring normalization in deep residual networks with concatenated rectified linear units
W. Shang, J. Chiu, and K. Sohn · 2017
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How to train a compact binary neural network with high accuracy?
W. Tang, G. Hua, and L. Wang · 2017
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All you need is beyond a good init: Exploring better solution for training extremely deep convolutional neural networks with orthonormality and modulation
D. Xie, J. Xiong, and S. Pu · 2017
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Accelerating binarized convolutional neural networks with software-programmable fpgas
R. Zhao, W. Song, W. Zhang, T. Xing, J.-H. Lin, M. Srivastava, R. Gupta, and Z. Zhang · 2017
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Understanding the impact of label granularity on cnn-based image classification
Z. Chen, R. Ding, T.-W. Chin, and D. Marculescu · 2018
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S. Zagoruyko and N. Komodakis · 2016
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Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients
S. Zhou, Y. Wu, Z. Ni, X. Zhou, H. Wen, and Y. Zou · 2016
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Deep learning with low precision by half-wave gaussian quantization
Z. Cai, X. He, J. Sun, and N. Vasconcelos · 2017
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An iot endpoint system-on-chip for secure and energy-efficient near-sensor analytics
F. Conti, R. Schilling, P. D. Schiavone, A. Pullini, D. Rossi, F. K. Gürkaynak, M. Muehlberghuber, M. Gautschi, I. Loi, G. Haugou, et al · 2017
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Lightnn: Filling the gap between conventional deep neural networks and binarized networks
R. Ding, Z. Liu, R. Shi, D. Marculescu, and R. Blanton · 2017
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Loss-aware binarization of deep networks
L. Hou, Q. Yao, and J. T. Kwok · 2017
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Centered weight normalization in accelerating training of deep neural networks
L. Huang, X. Liu, Y. Liu, B. Lang, and D. Tao · 2017
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Lightening the load with highly accurate storage-and energy-efficient lightnns
R. Ding, Z. Liu, R. Blanton, and D. Marculescu · 2018
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Syq: Learning symmetric quantization for efficient deep neural networks
J. Faraone, N. Fraser, M. Blott, and P. H. Leong · 2018
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Intelligence beyond the edge: Inference on intermittent embedded systems
G. Gobieski, N. Beckmann, and B. Lucia · 2018
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Amc: Automl for model compression and acceleration on mobile devices
Y. He, J. Lin, Z. Liu, H. Wang, L.-J. Li, and S. Han · 2018
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Bi-real net: Enhancing the performance of 1-bit cnns with improved representational capability and advanced training algorithm
Z. Liu, B. Wu, W. Luo, X. Yang, W. Liu, and K.-T. Cheng · 2018
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Apprentice: Using knowledge distillation techniques to improve low-precision network accuracy
A. Mishra and D. Marr · 2018
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WRPN: Wide reduced-precision networks
A. Mishra, E. Nurvitadhi, J. J. Cook, and D. Marr · 2018
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Model compression via distillation and quantization
A. Polino, R. Pascanu, and D. Alistarh · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen · 2018
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Linear backprop in non-linear networks
M. Yazdani · 2018
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A systematic study of binary neural networks’ optimisation
M. Alizadeh, J. Fernández-Marqués, N. D. Lane, and Y. Gal · 2019
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Adascale: Towards real-time video object detection using adaptive scaling
T.-W. Chin, R. Ding, and D. Marculescu · 2019
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