Conversion of Artificial Recurrent Neural Networks to Spiking Neural Networks for Low-power Neuromorphic Hardware

Abstract

In recent years the field of neuromorphic computing gained significant momentum, enabling systems that consume orders of magnitude less power than traditional ones. However, their wider use is still hindered by the lack of... [ view full abstract ]

Authors

  1. Peter U. Diehl (ETH Zurich)
  2. Guido Zarrella (The MITRE Corporation)
  3. Andrew Cassidy (IBM Research Austin)
  4. Bruno U. Pedroni (University of California San Diego)
  5. Emre Neftci (University of California Irvine)

Topic Area

Topics: Neuromorphic, or “brain inspired”, computing

Session

OS-01B » Neuromorphic 1 (10:15 - Monday, 17th October, Del Mar Ballroom AB)

Paper

ID004_ICRC2016.pdf

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