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Network quantization is one of the most hardware friendly techniques to enable the deployment of convolutional neural networks (CNNs) on low-power mobile devices.
Low-bit quantization of neural networks for efficient inference
Yoni Choukroun, Eli Kravchik, and Pavel Kisilev · 1902
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Low-bit quantization of neural networks for efficient inference
Yoni Choukroun, Eli Kravchik, and Pavel Kisilev · 1902
Earlier work this paper cites.
Evolving neural networks through augmenting topologies
Kenneth O. Stanley and Risto Miikkulainen · 2002
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On learning-based methods for design-space exploration with high-level synthesis
Hung-Yi Liu and Luca P. Carloni · 2013
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Exploiting linear structure within convolutional networks for efficient evaluation
Emily Denton, Wojciech Zaremba, Joan Bruna, Yann LeCun, and Rob Fergus · 2014
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Designing neural network architectures using reinforcement learning
Bowen Baker, Otkrist Gupta, Nikhil Naik, and Ramesh Raskar · 2016
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Squeezenet: Alexnet-level accuracy with 50x fewer parameters and <1mb model size
Forrest N. Iandola, Matthew W. Moskewicz, Khalid Ashraf, Song Han, William J. Dally, and Kurt Keutzer · 2016
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Bitwise neural networks
Minje Kim and Paris Smaragdis · 2016
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Fixed point quantization of deep convolutional networks
Darryl Lin, Sachin Talathi, and Sreekanth Annapureddy · 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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Network sketching: Exploiting binary structure in deep cnns
Yiwen Guo, Anbang Yao, Hao Zhao, and Yurong Chen · 2017
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Towards accurate binary convolutional neural network
Xiaofan Lin, Cong Zhao, and Wei Pan · 2017
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Ternary neural networks with fine-grained quantization
Naveen Mellempudi, Abhisek Kundu, Dheevatsa Mudigere, Dipankar Das, Bharat Kaul, and Pradeep Dubey · 2017
Cited alongside, same era.
A genetic programming approach to designing convolutional neural network architectures
Masanori Suganuma, Shinichi Shirakawa, and Tomoharu Nagao · 2017
Cited alongside, same era.
How to train a compact binary neural network with high accuracy?
Wei Tang, Gang Hua, and Liang Wang · 2017
Cited alongside, same era.
Incremental network quantization: Towards lossless cnns with low-precision weights
Aojun Zhou, Anbang Yao, Yiwen Guo, Lin Xu, and Yurong Chen · 2017
Cited alongside, same era.
Learning transferable architectures for scalable image recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V. Le · 2017
Cited alongside, same era.
Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
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Bit fusion: Bit-level dynamically composable architecture for accelerating deep neural network
H. Sharma, J. Park, N. Suda, L. Lai, B. Chau, V. Chandra, and H. Esmaeilzadeh · 2018
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Bayesian optimization for parameter tuning of the xor neural network
Lawrence Stewart and Mark Stalzer · 2018
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Towards evolutionary compression
Yunhe Wang, Chang Xu, Jiayan Qiu, Chao Xu, and Dacheng Tao · 2018
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Alternating multi-bit quantization for recurrent neural networks
Chen Xu, Jianqiang Yao, Zhouchen Lin, Wenwu Ou, Yuanbin Cao, Zhirong Wang, and Hongbin Zha · 2018
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Releq: A reinforcement learning approach for deep quantization of neural networks
Ahmed T. Elthakeb, Prannoy Pilligundla, Amir Yazdanbakhsh, Sean Kinzer, and Hadi Esmaeilzadeh · 2018
Cited alongside, same era.
Amc: Automl for model compression and acceleration on mobile devices
Yihui He, Ji Lin, Zhijian Liu, Hanrui Wang, Li-Jia Li, and Song Han · 2018
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
Cited alongside, same era.
Neural architecture search with bayesian optimisation and optimal transport
Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabás Póczos, and Eric Xing · 2018
Cited alongside, same era.
Quantizing deep convolutional networks for efficient inference: A whitepaper
Raghuraman Krishnamoorthi · 2018
Cited alongside, same era.
Discovering low-precision networks close to full-precision networks for efficient embedded inference
Jeffrey L. McKinstry, Steven K. Esser, Rathinakumar Appuswamy, Deepika Bablani, John V. Arthur, Izzet B. Yildiz, and Dharmendra S. Modha · 2018
Cited alongside, same era.
Data-efficient hierarchical reinforcement learning
Ofir Nachum, Shane Gu, Honglak Lee, and Sergey Levine · 2018
Cited alongside, same era.
Lq-nets: Learned quantization for highly accurate and compact deep neural networks
Dongqing Zhang, Jiaolong Yang, Dongqiangzi Ye, and Gang Hua · 2018
Later among the works it cites.
Compiling kb-sized machine learning models to tiny iot devices
Sridhar Gopinath, Nikhil Ghanathe, Vivek Seshadri, and Rahul Sharma · 2019
Closest in time.
Fully quantized network for object detection
Rundong Li, Yan Wang, Feng Liang, Hongwei Qin, Junjie Yan, and Rui Fan · 2019
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Post training weight compression with distribution-based filter-wise quantization step
S. Sasaki, A. Maki, D. Miyashita, and J. Deguchi · 2019
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Haq: Hardware-aware automated quantization with mixed precision
Kuan Wang, Zhijian Liu, Yujun Lin, Ji Lin, and Song Han · 2019
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Kcnn: Kernel-wise quantization to remarkably decrease multiplications in convolutional neural network
Linghua Zeng, Zhangcheng Wang, and Xinmei Tian · 2019
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Primal: Power inference using machine learning
Yuan Zhou, Haoxing Ren, Yanqing Zhang, Ben Keller, Brucek Khailany, and Zhiru Zhang · 2019
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