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Convolutional neural networks (CNNs) achieve state-of-the-art accuracy in a variety of tasks in computer vision and beyond.
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 · 1902
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Regularizing activation distribution for training binarized deep networks
Ruizhou Ding, Ting-Wu Chin, Zeye Liu, and Diana Marculescu · 1904
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Feathernet: An accelerated convolutional neural network design for resource-constrained fpgas
Raghid Morcel, Hazem Hajj, Mazen A. R. Saghir, Haitham Akkary, Hassan Artail, Rahul Khanna, and Anil Keshavamurthy · 1936
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High-rate transform coding: how high is high, and does it matter?
Vivek K. Goyal · 2000
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Asymptotic average redundancy of huffman (and shannon-fano) block codes
Wojciech Szpankowski · 2000
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Theoretical foundations of transform coding
Vivek K. Goyal · 2001
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Deep learning with limited numerical precision
Suyog Gupta, Ankur Agrawal, Kailash Gopalakrishnan, and Pritish Narayanan · 2015
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ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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Michael Cogswell, Faruk Ahmed, Ross B. Girshick, Larry Zitnick, and Dhruv Batra · 2016
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Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding
Song Han, Huizi Mao, and William J Dally · 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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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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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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Deep learning with low precision by half-wave gaussian quantization
Zhaowei Cai, Xiaodong He, Jian Sun, and Nuno Vasconcelos · 2017
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Benoit Jacob and Pete Warden · 2017
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In-datacenter performance analysis of a tensor processing unit
Norman P. Jouppi, Cliff Young, Nishant Patil, David A. Patterson, Gaurav Agrawal, Raminder Bajwa, Sarah Bates, Suresh Bhatia, Nan Boden, Al Borchers, Rick Boyle, Pierre-luc Cantin, Clifford Chao, Chris Clark, Jeremy Coriell, Mike Daley, Matt Dau, Jeffrey Dean, Ben Gelb, Tara Vazir Ghaemmaghami, Rajendra Gottipati, William Gulland, Robert Hagmann, Richard C. Ho, Doug Hogberg, John Hu, Robert Hundt, Dan Hurt, Julian Ibarz, Aaron Jaffey, Alek Jaworski, Alexander Kaplan, Harshit Khaitan, Andy Koch, Naveen Kumar, Steve Lacy, James Laudon, James Law, Diemthu Le, Chris Leary, Zhuyuan Liu, Kyle Lucke, Alan Lundin, Gordon MacKean, Adriana Maggiore, Maire Mahony, Kieran Miller, Rahul Nagarajan, Ravi Narayanaswami, Ray Ni, Kathy Nix, Thomas Norrie, Mark Omernick, Narayana Penukonda, Andy Phelps, Jonathan Ross, Amir Salek, Emad Samadiani, Chris Severn, Gregory Sizikov, Matthew Snelham, Jed Souter, Dan Steinberg, Andy Swing, Mercedes Tan, Gregory Thorson, Bo Tian, Horia Toma, Erick Tuttle, Vijay Vasudevan, Richard Walter, Walter Wang, Eric Wilcox, and Doe Hyun Yoon · 2017
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8-bit inference with tensorrt, 2017
Szymon Migacz · 2017
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Learning sparse neural networks through l0 regularization
Christos Louizos, Max Welling, and Diederik P. Kingmao · 2018
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Wrpn: Wide reduced-precision networks
Asit Mishra, Eriko Nurvitadhi, Jeffrey J Cook, and Debbie Marr · 2018
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Squantizer: Simultaneous learning for both sparse and low-precision neural networks
Mi Sun Park, Xiaofan Xu, and Cormac Brick · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
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A quantization-friendly separable convolution for mobilenets, 2018
Tao Sheng, Chen Feng, Shaojie Zhuo, Xiaopeng Zhang, Liang Shen, and Mickey Aleksic · 2018
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Faster gaze prediction with dense networks and fisher pruning
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Exploring heterogeneous algorithms for accelerating deep convolutional neural networks on fpgas
Qingcheng Xiao, Yun Liang, Liqiang Lu, Shengen Yan, and Yu-Wing Tai · 2017
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A method to estimate the energy consumption of deep neural networks
Tien-Ju Yang, Yu-Hsin Chen, Joel Emer, and Vivienne Sze · 2017
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Selective data transfer from drams for cnns
Anaam Ansari and Tokunbo Ogunfunmi · 2018
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Post-training 4-bit quantization of convolution networks for rapid-deployment, 2018
Ron Banner, Yury Nahshan, Elad Hoffer, and Daniel Soudry · 2018
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Uniq: Uniform noise injection for the quantization of neural networks
Chaim Baskin, Eli Schwartz, Evgenii Zheltonozhskii, Natan Liss, Raja Giryes, Alex M Bronstein, and Avi Mendelson · 2018
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Data bandwidth reduction in deep neural network socs using history buffer and huffman coding
Mahesh Chandra · 2018
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Highly efficient 8-bit low precision inference of convolutional neural networks with intelcaffe
Jiong Gong, Haihao Shen, Guoming Zhang, Xiaoli Liu, Shane Li, Ge Jin, Niharika Maheshwari, Evarist Fomenko, and Eden Segal · 2018
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Dnn feature map compression using learned representation over gf(2)
Denis Gudovskiy, Alec Hodgkinson, and Luca Rigazio · 2018
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Lucas Theis, Iryna Korshunova, Alykhan Tejani, and Ferenc Huszár · 2018
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Lq-nets: Learned quantization for highly accurate and compact deep neural networks
Dongqing Zhang, Jiaolong Yang, Dongqiangzi Ye, and Gang Hua · 2018
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Extended bit-plane compression for convolutional neural network accelerators
Lukas Cavigelli and Luca Benini · 2019
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Low-bit quantization of neural networks for efficient inference, 2019
Yoni Choukroun, Eli Kravchik, Fan Yang, and Pavel Kisilev · 2019
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Lutnet: Rethinking inference in fpga soft logic
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Swalp: Stochastic weight averaging in low-precision training
Guandao Yang, Tianyi Zhang, Polina Kirichenko, Junwen Bai, Andrew Gordon Wilson, and Christopher De Sa · 2019
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