Variability-tolerant Convolutional Neural Network for Pattern Recognition applications based on OxRAM synapses

@article{Garbin2014VariabilitytolerantCN,
  title={Variability-tolerant Convolutional Neural Network for Pattern Recognition applications based on OxRAM synapses},
  author={Daniele Garbin and Olivier Bichler and Elisa Vianello and Quentin Rafhay and Christian Gamrat and Luca Perniola and G{\'e}rard Ghibaudo and Barbara DeSalvo},
  journal={2014 IEEE International Electron Devices Meeting},
  year={2014},
  pages={28.4.1-28.4.4}
}
Software implementations of artificial Convolutional Neural Networks (CNNs), taking inspiration from biology, are at the state-of-the-art for Pattern Recognition (PR) applications and they are successfully used in commercial products [1]. However, they require power-hungry CPU/GPU to perform convolution operations based on computationally expensive sums of multiplications. This hinders their integration in portable devices. Some full CMOS-based hardware implementations of CNN have been… CONTINUE READING
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