• Computer Science
• Published in ECCV 2016
• DOI:10.1007/978-3-319-46493-0_32

XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks

@inproceedings{Rastegari2016XNORNetIC,
  title={XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks},
  author={Mohammad Rastegari and Vicente Ordonez and Joseph Redmon and Ali Farhadi},
  booktitle={ECCV},
  year={2016}
}

• Mohammad Rastegari, Vicente Ordonez, +1 author Ali Farhadi

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We propose two efficient approximations to standard convolutional neural networks: Binary-Weight-Networks and XNOR-Networks. [...] Key Result This results in 58\(\times \) faster convolutional operations (in terms of number of the high precision operations) and 32\(\times \) 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…Expand Abstract

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