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Deep neural networks (DNNs) have become ubiquitous in machine learning, but their energy consumption remains problematically high.
Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition
Johannes Stallkamp, Marc Schlipsing, Jan Salmen, and Christian Igel · 2012
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Compressing deep convolutional networks using vector quantization
Yunchao Gong, Liu Liu, Ming Yang, and Lubomir Bourdev · 2014
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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K. Simonyan and A. Zisserman · 2015
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Eyeriss: A spatial architecture for energy-efficient dataflow for convolutional neural networks
Yu-Hsin Chen, Joel Emer, and Vivienne Sze · 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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Xnor-net: Imagenet classification using binary convolutional neural networks
Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi · 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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Significance driven hybrid 8t-6t sram for energy-efficient synaptic storage in artificial neural networks
Gopalakrishnan Srinivasan, Parami Wijesinghe, Syed Shakib Sarwar, Akhilesh Jaiswal, and Kaushik Roy · 2016
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Quantized convolutional neural networks for mobile devices
Jiaxiang Wu, Cong Leng, Yuhang Wang, Qinghao Hu, and Jian Cheng · 2016
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On characterizing near-threshold sram failures in finfet technology
Shrikanth Ganapathy, John Kalamatianos, Keith Kasprak, and Steven Raasch · 2017
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Universal adversarial perturbations
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, and Pascal Frossard · 2017
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Designing energy-efficient convolutional neural networks using energy-aware pruning
Tien-Ju Yang, Yu-Hsin Chen, and Vivienne Sze · 2017
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Synthesizing robust adversarial examples
Batch-shaping for learning conditional channel gated networks
Babak Ehteshami Bejnordi, Tijmen Blankevoort, and Max Welling · 2020
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Input-aware dynamic backdoor attack
Tuan Anh Nguyen and Anh Tran · 2020
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A systematic methodology for characterizing scalability of dnn accelerators using scale-sim
Ananda Samajdar, Jan Moritz Joseph, Yuhao Zhu, Paul Whatmough, Matthew Mattina, and Tushar Krishna · 2020
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Adversarial weight perturbation helps robust generalization
Dongxian Wu, Shu-Tao Xia, and Yisen Wang · 2020
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Automatic neural network compression by sparsity-quantization joint learning: A constrained optimization-based approach
Haichuan Yang, Shupeng Gui, Yuhao Zhu, and Ji Liu · 2020
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Bit error robustness for energy-efficient dnn accelerators
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Anish Athalye, Logan Engstrom, Andrew Ilyas, and Kevin Kwok · 2018
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Matic: Learning around errors for efficient low-voltage neural network accelerators
Sung Kim, Patrick Howe, Thierry Moreau, Armin Alaghi, Luis Ceze, and Visvesh Sathe · 2018
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Resilient low voltage accelerators for high energy efficiency
Nandhini Chandramoorthy, Karthik Swaminathan, Martin Cochet, Arun Paidimarri, Schuyler Eldridge, Rajiv V. Joshi, Matthew M. Ziegler, Alper Buyuktosunoglu, and Pradip Bose · 2019
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Eden: Enabling energy-efficient, high-performance deep neural network inference using approximate dram
Skanda Koppula, Lois Orosa, A Giray Yağlıkçı, Roknoddin Azizi, Taha Shahroodi, Konstantinos Kanellopoulos, and Onur Mutlu · 2019
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Theoretically principled trade-off between robustness and accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric Xing, Laurent El Ghaoui, and Michael Jordan · 2019
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Ecc: Platform-independent energy-constrained deep neural network compression via a bilinear regression model
Haichuan Yang, Yuhao Zhu, and Ji Liu
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Energy-constrained compression for deep neural networks via weighted sparse projection and layer input masking
Haichuan Yang, Yuhao Zhu, and Ji Liu
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David Stutz, Nandhini Chandramoorthy, Matthias Hein, and Bernt Schiele · 2021
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A survey of quantization methods for efficient neural network inference
Amir Gholami, Sehoon Kim, Zhen Dong, Zhewei Yao, Michael W Mahoney, and Kurt Keutzer · 2022
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Learning representation for clustering via prototype scattering and positive sampling
Zhizhong Huang, Jie Chen, Junping Zhang, and Hongming Shan · 2022
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Improving robustness against stealthy weight bit-flip attacks by output code matching
Ozan Özdenizci and Robert Legenstein · 2022
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ptflops: a flops counting tool for neural networks in pytorch framework, 2018-2023
Vladislav Sovrasov · 2023
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