Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computing

@article{Esser2016ConvolutionalNF,
  title={Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computing},
  author={Steven K. Esser and Paul Merolla and John V. Arthur and Andrew S. Cassidy and Rathinakumar Appuswamy and Alexander Andreopoulos and David J. Berg and Jeffrey L. McKinstry and Timothy Melano and Davis Barch and Carmelo di Nolfo and Pallab Datta and Arnon Amir and Brian Taba and Myron Flickner and Dharmendra S. Modha},
  journal={Proceedings of the National Academy of Sciences of the United States of America},
  year={2016},
  volume={113 41},
  pages={11441-11446}
}
Deep networks are now able to achieve human-level performance on a broad spectrum of recognition tasks. Independently, neuromorphic computing has now demonstrated unprecedented energy-efficiency through a new chip architecture based on spiking neurons, low precision synapses, and a scalable communication network. Here, we demonstrate that neuromorphic computing, despite its novel architectural primitives, can implement deep convolution networks that (i) approach state-of-the-art classification… CONTINUE READING
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