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In this paper, we propose a novel network design mechanism for efficient embedded computing.
Labeled faces in the wild: A database forstudying face recognition in unconstrained environments
Gary B Huang, Marwan Mattar, Tamara Berg, and Eric Learned-Miller · 2008
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CNP: an fpga-based processor for convolutional networks
Clément Farabet, Cyril Poulet, Jefferson Y. Han, and Yann LeCun · 2009
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Vision meets robotics: The kitti dataset
Andreas Geiger, Philip Lenz, Christoph Stiller, and Raquel Urtasun · 2013
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Diannao: A small-footprint high-throughput accelerator for ubiquitous machine-learning
Tianshi Chen, Zidong Du, Ninghui Sun, Jia Wang, Chengyong Wu, Yunji Chen, and Olivier Temam · 2014
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Depth map prediction from a single image using a multi-scale deep network
David Eigen, Christian Puhrsch, and Rob Fergus · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev, Ross B. Girshick, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick · 2014
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Deep learning with limited numerical precision
Suyog Gupta, Ankur Agrawal, Kailash Gopalakrishnan, and Pritish Narayanan · 2015
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Optimizing fpga-based accelerator design for deep convolutional neural networks
Chen Zhang, Peng Li, Guangyu Sun, Yijin Guan, Bingjun Xiao, and Jason Cong · 2015
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The cityscapes dataset for semantic urban scene understanding
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele · 2016
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Angel-eye: A complete design flow for mapping cnn onto customized hardware
Kaiyuan Guo, Lingzhi Sui, Jiantao Qiu, Song Yao, Song Han, Yu Wang, and Huazhong Yang · 2016
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Ms-celeb-1m: A dataset and benchmark for large-scale face recognition
Yandong Guo, Lei Zhang, Yuxiao Hu, Xiaodong He, and Jianfeng Gao · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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SqueezeNet: Alexnet-level accuracy with 50x fewer parameters and < < 0.5mb model size
Forrest N. Iandola, Song Han, Matthew W. Moskewicz, Khalid Ashraf, William J. Dally, and Kurt Keutzer · 2016
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Enet: A deep neural network architecture for real-time semantic segmentation
Adam Paszke, Abhishek Chaurasia, Sangpil Kim, and Eugenio Culurciello · 2016
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Minerva: Enabling low-power, highly-accurate deep neural network accelerators
Brandon Reagen, Paul Whatmough, Robert Adolf, Saketh Rama, Hyunkwang Lee, Sae Kyu Lee, José Miguel Hernández-Lobato, Gu-Yeon Wei, and David Brooks · 2016
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Frontal to profile face verification in the wild
Soumyadip Sengupta, Jun-Cheng Chen, Carlos Castillo, Vishal M Patel, Rama Chellappa, and David W Jacobs · 2016
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Neural architecture search with reinforcement learning
Barret Zoph and Quoc V. Le · 2016
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Segnet: A deep convolutional encoder-decoder architecture for image segmentation
Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla · 2017
Cited alongside, same era.
Compiling deep learning models for custom hardware accelerators
Andre Xian Ming Chang, Aliasger Zaidy, Vinayak Gokhale, and Eugenio Culurciello · 2017
Cited alongside, same era.
Xception: Deep learning with depthwise separable convolutions
François Chollet · 2017
Cited alongside, same era.
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
Cited alongside, same era.
In-datacenter performance analysis of a tensor processing unit
Norman P Jouppi, Cliff Young, Nishant Patil, David Patterson, Gaurav Agrawal, Raminder Bajwa, Sarah Bates, Suresh Bhatia, Nan Boden, Al Borchers, et al · 2017
Arcface: Additive angular margin loss for deep face recognition
Jiankang Deng, Jia Guo, Niannan Xue, and Stefanos Zafeiriou · 2018
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Ucnn: Exploiting computational reuse in deep neural networks via weight repetition
Kartik Hegde, Jiyong Yu, Rohit Agrawal, Mengjia Yan, Michael Pellauer, and Christopher W Fletcher · 2018
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Shufflenet v2: Practical guidelines for efficient cnn architecture design
Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, and Jian Sun · 2018
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Efficient neural architecture search via parameter sharing
Hieu Pham, Melody Y. Guan, Barret Zoph, Quoc V. Le, and Jeff Dean · 2018
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Regularized evolution for image classifier architecture search
Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V. Le · 2018
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Cited alongside, same era.
Feature pyramid networks for object detection
Tsung-Yi Lin, Piotr Dollár, Ross B. Girshick, Kaiming He, Bharath Hariharan, and Serge J. Belongie · 2017
Cited alongside, same era.
Dadiannao: A neural network supercomputer
Tao Luo, Shaoli Liu, Ling Li, Yuqing Wang, Shijin Zhang, Tianshi Chen, Zhiwei Xu, Olivier Temam, and Yunji Chen · 2017
Cited alongside, same era.
Optimizing loop operation and dataflow in fpga acceleration of deep convolutional neural networks
Yufei Ma, Yu Cao, Sarma Vrudhula, and Jae-sun Seo · 2017
Cited alongside, same era.
Agedb: the first manually collected, in-the-wild age database
Stylianos Moschoglou, Athanasios Papaioannou, Christos Sagonas, Jiankang Deng, Irene Kotsia, and Stefanos Zafeiriou · 2017
Cited alongside, same era.
Level playing field for million scale face recognition
Aaron Nech and Ira Kemelmacher-Shlizerman · 2017
Cited alongside, same era.
fpgaconvnet: Automated mapping of convolutional neural networks on fpgas
Stylianos I Venieris and Christos-Savvas Bouganis · 2017
Cited alongside, same era.
Exploring heterogeneous algorithms for accelerating deep convolutional neural networks on fpgas
Qingcheng Xiao, Yun Liang, Liqiang Lu, Shengen Yan, and Yu-Wing Tai · 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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Igcv3: Interleaved low-rank group convolutions for efficient deep neural networks
Ke Sun, Mingjie Li, Dong Liu, and Jingdong Wang · 2018
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Toolflows for mapping convolutional neural networks on fpgas: A survey and future directions
Stylianos I Venieris, Alexandros Kouris, and Christos-Savvas Bouganis · 2018
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Fbnet: Hardware-aware efficient convnet design via differentiable neural architecture search
Bichen Wu, Xiaoliang Dai, Peizhao Zhang, Yanghan Wang, Fei Sun, Yiming Wu, Yuandong Tian, Peter Vajda, Yangqing Jia, and Kurt Keutzer · 2018
Later among the works it cites.
Interleaved structured sparse convolutional neural networks
Guotian Xie, Jingdong Wang, Ting Zhang, Jianhuang Lai, Richang Hong, and Guo-Jun Qi · 2018
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Bisenet: Bilateral segmentation network for real-time semantic segmentation
Changqian Yu, Jingbo Wang, Chao Peng, Changxin Gao, Gang Yu, and Nong Sang · 2018
Later among the works it cites.
Shufflenet: An extremely efficient convolutional neural network for mobile devices
Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, and Jian Sun · 2018
Later among the works it cites.
ProxylessNAS: Direct neural architecture search on target task and hardware
Han Cai, Ligeng Zhu, and Song Han · 2019
Closest in time.
Panoptic feature pyramid networks
Alexander Kirillov, Ross B. Girshick, Kaiming He, and Piotr Dollár · 2019
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DARTS: Differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 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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Dnnvm: End-to-end compiler leveraging heterogeneous optimizations on fpga-based cnn accelerators
Yu Xing, Shuang Liang, Lingzhi Sui, Xijie Jia, Jiantao Qiu, Xin Liu, Yushun Wang, Yu Wang, and Yi Shan · 2019
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