Low-Power Neuromorphic Hardware for Signal Processing Applications: A review of architectural and system-level design approaches

@article{Rajendran2019LowPowerNH,
  title={Low-Power Neuromorphic Hardware for Signal Processing Applications: A review of architectural and system-level design approaches},
  author={Bipin Rajendran and Abu Sebastian and Michael Schmuker and Narayan Srinivasa and Evangelos Eleftheriou},
  journal={IEEE Signal Processing Magazine},
  year={2019},
  volume={36},
  pages={97-110}
}
  • Bipin Rajendran, Abu Sebastian, +2 authors Evangelos Eleftheriou
  • Published 2019
  • Computer Science
  • IEEE Signal Processing Magazine
  • Machine learning has emerged as the dominant tool for implementing complex cognitive tasks that require supervised, unsupervised, and reinforcement learning. While the resulting machines have demonstrated in some cases even superhuman performance, their energy consumption has often proved to be prohibitive in the absence of costly supercomputers. Most state-of-the-art machine-learning solutions are based on memoryless models of neurons. This is unlike the neurons in the human brain that encode… CONTINUE READING

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