• DOI:10.1109/iscas45731.2020.9180634/video
Corpus ID: 215828246

MuBiNN: Multi-Level Binarized Recurrent Neural Network for EEG signal Classification

@article{Mirsalari2020MuBiNNMB,
  title={MuBiNN: Multi-Level Binarized Recurrent Neural Network for EEG signal Classification},
  author={Seyed Ahmad Mirsalari and S. Sinaei and M. Salehi and M. Daneshtalab},
  journal={ArXiv},
  year={2020},
  volume={abs/2004.08914}
}

Seyed Ahmad Mirsalari, S. Sinaei, +1 author M. Daneshtalab

Published 2020

Computer Science, Engineering

ArXiv

Recurrent Neural Networks (RNN) are widely used for learning sequences in applications such as EEG classification. Complex RNNs could be hardly deployed on wearable devices due to their computation and memory-intensive processing patterns. Generally, reduction in precision leads much more efficiency and binarized RNNs are introduced as energy-efficient solutions. However, naive binarization methods lead to significant accuracy loss in EEG classification. In this paper, we propose a multi-level… CONTINUE READING

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