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The vast majority of processors in the world are actually microcontroller units (MCUs), which find widespread use performing simple control tasks in applications ranging from automobiles to medical devices and office equipment.
The state of sparsity in deep neural networks
Trevor Gale, Erich Elsen, and Sara Hooker · 1902
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Optimal brain damage
Yann LeCun, John S Denker, and Sara A Solla · 1990
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, Patrick Haffner, et al · 1998
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An introduction to variational methods for graphical models
Michael I. Jordan, Zoubin Ghahramani, Tommi S. Jaakkola, and Lawrence K. Saul · 1999
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Sparse bayesian learning and the relevance vector machine
Michael E Tipping · 2001
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Gaussian processes in machine learning
Carl Edward Rasmussen · 2003
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A statistical approach to texture classification from single images
Manik Varma and Andrew Zisserman · 2005
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Character recognition in natural images
Teófilo Emídio de Campos, Bodla Rakesh Babu, and Manik Varma · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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The internet of things: A survey
Luigi Atzori, Antonio Iera, and Giacomo Morabito · 2010
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Internet of things (iot): A vision, architectural elements, and future directions
Jayavardhana Gubbi, Rajkumar Buyya, Slaven Marusic, and Marimuthu Palaniswami · 2013
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2014
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The ‘Internet of Things’ is now
Francois Meunier, Adam Wood, Keith Weiss, Katy Huberty, Simon Flannery, Joseph Moore, Craig Hettenbach, and Bill Lu · 2014
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Rigid-motion scattering for image classification
Laurent Sifre and Stéphane Mallat · 2014
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Raiders of the lost architecture: Kernels for bayesian optimization in conditional parameter spaces
Kevin Swersky, David Duvenaud, Jasper Snoek, Frank Hutter, and Michael A Osborne · 2014
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Variational dropout and the local reparameterization trick
Durk P Kingma, Tim Salimans, and Max Welling · 2015
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Pruning decision forests
O Dekel, C Jacobbs, and L Xiao · 2016
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EIE: efficient inference engine on compressed deep neural network
Song Han, Xingyu Liu, Huizi Mao, Jing Pu, Ardavan Pedram, Mark A Horowitz, and William J Dally · 2016
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Pruning convolutional neural networks for resource efficient transfer learning
Pavlo Molchanov, Stephen Tyree, Tero Karras, Timo Aila, and Jan Kautz · 2016
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How to train deep variational autoencoders and probabilistic ladder networks
Casper Kaae Sønderby, Tapani Raiko, Lars Maaløe, Søren Kaae Sønderby, and Ole Winther · 2016
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Network morphism
Tao Wei, Changhu Wang, Yong Rui, and Chang Wen Chen · 2016
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Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le · 2016
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Protonn: Compressed and accurate knn for resource-scarce devices
Chirag Gupta, Arun Sai Suggala, Ankit Goyal, Harsha Vardhan Simhadri, Bhargavi Paranjape, Ashish Kumar, Saurabh Goyal, Raghavendra Udupa, Manik Varma, and Prateek Jain · 2017
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Channel pruning for accelerating very deep neural networks
Yihui He, Xiangyu Zhang, and Jian Sun · 2017
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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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URL https://www.statista.com/statistics/865846/worldwide-discrete-gpus-shipment/
Global shipments of discrete graphics processing units from 2015 to 2018 (in million units) · 2019
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URL https://www.pugetsystems.com/labs/hpc/Numerical-Computing-Performance-of-3-Intel-8-core-CPUs-i9---9900K-vs-i7-9800X-vs-Xeon-2145W-1339/
Numerical Computing Performance of Intel 8-core CPUs, a · 2019
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URL https://en.wikipedia.org/wiki/List_of_Intel_Core_i9_microprocessors
List of Intel Core i9 Microprocessors, Wikipedia Article, b · 2019
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URL https://github.com/ARMmbed/mbed-cli
Arm mbed-cli · 2019
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URL https://en.wikipedia.org/wiki/Micro_Bit
Micro Bit Hardware Specification, Wikipedia Article · 2019
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URL https://microsoft.github.io/ELL/
Microsoft Embedded Learning Library · 2019
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Resource-efficient machine learning in 2 kb ram for the internet of things
Ashish Kumar, Saurabh Goyal, and Manik Varma · 2017
Cited alongside, same era.
Bayesian compression for deep learning
Christos Louizos, Karen Ullrich, and Max Welling · 2017
Cited alongside, same era.
Variational dropout sparsifies deep neural networks
Dmitry Molchanov, Arsenii Ashukha, and Dmitry Vetrov · 2017
Cited alongside, same era.
Designing energy-efficient convolutional neural networks using energy-aware pruning
Tien-Ju Yang, Yu-Hsin Chen, and Vivienne Sze · 2017
Cited alongside, same era.
“Learning-Compression” Algorithms for Neural Net Pruning
Miguel Á. Carreira-Perpiñán and Yerlan Idelbayev · 2018
Cited alongside, same era.
BOHB: robust and efficient hyperparameter optimization at scale
Stefan Falkner, Aaron Klein, and Frank Hutter · 2018
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Eduardo C Garrido-Merchán and Daniel Hernández-Lobato · 2018
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URL https://en.wikipedia.org/wiki/Pixel_(smartphone)
Pixel (Smartphone) Hardware Specification, Wikipedia Article · 2019
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URL https://en.wikipedia.org/wiki/Raspberry_Pi
Raspberry Pi Hardware Specification, Wikipedia Article · 2019
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URL https://en.wikipedia.org/wiki/STM32
STM32 Hardware Specification, Wikipedia · 2019
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URL https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/quantize
TensorFlow Quantization-Aware Training · 2019
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URL https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/experimental/micro
TensorFlow Lite for Microcontrollers · 2019
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URL http://utensor.ai/
uTensor · 2019
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URL https://docs.google.com/document/u/2/d/e/2PACX-1vStp3uPhxJB0YTwL4T__Q5xjclmrj6KRs55xtMJrCyi82GoyHDp2X0KdhoYcyjEzKe4v75WBqPObdkP/pub
Visual Wake Words Challenge, CVPR 2019 · 2019
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URL https://petewarden.com/2018/06/11/why-the-future-of-machine-learning-is-tiny/
Why the Future of Machine Learning is Tiny · 2019
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https://en.wikipedia.org/wiki/Microcontroller
Microcontroller, Wikipedia Article · 2019
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ProxylessNAS: Direct neural architecture search on target task and hardware
Han Cai, Ligeng Zhu, and Song Han · 2019
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2019
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Single path one-shot neural architecture search with uniform sampling
Zichao Guo, Xiangyu Zhang, Haoyuan Mu, Wen Heng, Zechun Liu, Yichen Wei, and Jian Sun · 2019
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Literature on Neural Architecture Search at AutoML.org at Freiburg
Marius Lindauer · 2019
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Single-path nas: Designing hardware-efficient convnets in less than 4 hours
Dimitrios Stamoulis, Ruizhou Ding, Di Wang, Dimitrios Lymberopoulos, Bodhi Priyantha, Jie Liu, and Diana Marculescu · 2019
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HAQ: hardware-aware automated quantization
Kuan Wang, Zhijian Liu, Yujun Lin, Ji Lin, and Song Han · 2019
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