Memristor-Based Multilayer Neural Networks With Online Gradient Descent Training

@article{Soudry2015MemristorBasedMN,
  title={Memristor-Based Multilayer Neural Networks With Online Gradient Descent Training},
  author={Daniel Soudry and Dotan Di Castro and Asaf Gal and Avinoam Kolodny and Shahar Kvatinsky},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2015},
  volume={26},
  pages={2408-2421}
}
Learning in multilayer neural networks (MNNs) relies on continuous updating of large matrices of synaptic weights by local rules. Such locality can be exploited for massive parallelism when implementing MNNs in hardware. However, these update rules require a multiply and accumulate operation for each synaptic weight, which is challenging to implement compactly using CMOS. In this paper, a method for performing these update operations simultaneously (incremental outer products) using memristor… CONTINUE READING
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