XNOR-Net

XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks. We propose two efficient approximations to standard convolutional neural networks: Binary-Weight-Networks and XNOR-Networks. In Binary-Weight-Networks, the filters are approximated with binary values resulting in 32x memory saving. In XNOR-Networks, both the filters and the input to convolutional layers are binary. XNOR-Networks approximate convolutions using primarily binary operations. This results in 58x faster convolutional operations and 32x memory savings. XNOR-Nets offer the possibility of running state-of-the-art networks on CPUs (rather than GPUs) in real-time. Our binary networks are simple, accurate, efficient, and work on challenging visual tasks. We evaluate our approach on the ImageNet classification task. The classification accuracy with a Binary-Weight-Network version of AlexNet is only 2.9% less than the full-precision AlexNet (in top-1 measure). We compare our method with recent network binarization methods, BinaryConnect and BinaryNets, and outperform these methods by large margins on ImageNet, more than 16% in top-1 accuracy.


References in zbMATH (referenced in 21 articles )

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  1. Chen, Zejia; Duan, Fabing; Chapeau-Blondeau, François; Abbott, Derek: Training threshold neural networks by extreme learning machine and adaptive stochastic resonance (2022)
  2. Amir, Guy; Wu, Haoze; Barrett, Clark; Katz, Guy: An SMT-based approach for verifying binarized neural networks (2021)
  3. Ashbrock, Jonathan; Powell, Alexander M.: Stochastic Markov gradient descent and training low-bit neural networks (2021)
  4. Evans, Richard; Bošnjak, Matko; Buesing, Lars; Ellis, Kevin; Pfau, David; Kohli, Pushmeet; Sergot, Marek: Making sense of raw input (2021)
  5. Liu, Chunlei; Ding, Wenrui; Hu, Yuan; Zhang, Baochang; Liu, Jianzhuang; Guo, Guodong; Doermann, David: Rectified binary convolutional networks with generative adversarial learning (2021)
  6. Lybrand, Eric; Saab, Rayan: A greedy algorithm for quantizing neural networks (2021)
  7. Qi, Zhongang; Khorram, Saeed; Fuxin, Li: Embedding deep networks into visual explanations (2021)
  8. Wu, Mike; Parbhoo, Sonali; Hughes, Michael C.; Roth, Volker; Doshi-Velez, Finale: Optimizing for interpretability in deep neural networks with tree regularization (2021)
  9. Dong, Yinpeng; Ni, Renkun; Li, Jianguo; Chen, Yurong; Su, Hang; Zhu, Jun: Stochastic quantization for learning accurate low-bit deep neural networks (2019)
  10. Liu, Haomiao; Wang, Ruiping; Shan, Shiguang; Chen, Xilin: Deep supervised hashing for fast image retrieval (2019)
  11. Wang, Bao; Yin, Penghang; Bertozzi, Andrea Louise; Brantingham, P. Jeffrey; Osher, Stanley Joel; Xin, Jack: Deep learning for real-time crime forecasting and its ternarization (2019)
  12. Yin, Penghang; Zhang, Shuai; Lyu, Jiancheng; Osher, Stanley; Qi, Yingyong; Xin, Jack: Blended coarse gradient descent for full quantization of deep neural networks (2019)
  13. Zhao, Yulin; Wang, Donghui; Wang, Leiou: Convolution accelerator designs using fast algorithms (2019)
  14. Hubara, Itay; Courbariaux, Matthieu; Soudry, Daniel; El-Yaniv, Ran; Bengio, Yoshua: Quantized neural networks: training neural networks with low precision weights and activations (2018)
  15. Needell, Deanna; Saab, Rayan; Woolf, Tina: Simple classification using binary data (2018)
  16. Yin, Penghang; Zhang, Shuai; Lyu, Jiancheng; Osher, Stanley; Qi, Yingyong; Xin, Jack: BinaryRelax: a relaxation approach for training deep neural networks with quantized weights (2018)
  17. Zhao, Yulin; Wang, Donghui; Wang, Leiou; Liu, Peng: A faster algorithm for reducing the computational complexity of convolutional neural networks (2018)
  18. Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications (2017) arXiv
  19. Rachkovskij, D. A.: Index structures for fast similarity search for binary vectors (2017)
  20. Rawat, Waseem; Wang, Zenghui: Deep convolutional neural networks for image classification: a comprehensive review (2017)

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