Supervised learning with organic memristor devices and prospects for neural crossbar arrays

@article{Bennett2015SupervisedLW,
  title={Supervised learning with organic memristor devices and prospects for neural crossbar arrays},
  author={Christopher H. Bennett and Djaafar Chabi and Theo Cabaret and Bruno Jousselme and Vincent Derycke and Damien Querlioz and Jacques-Olivier Klein},
  journal={Proceedings of the 2015 IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH´15)},
  year={2015},
  pages={181-186}
}
The integration of memristive nanodevices within transistor-based electronic systems offers the potential for computing structures smaller, lower power and cheaper than traditional high-performance systems. Among emerging memristive technologies, a novel device based on organic materials distinguishes itself, in that it can feature several threshold voltages on the same die, and possesses unipolar behavior. In this work, we highlight that these two features can be beneficial for neural network… CONTINUE READING

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