Neuromorphic scaling advantages for energy-efficient random walk computation

@article{Smith2021NeuromorphicSA,
  title={Neuromorphic scaling advantages for energy-efficient random walk computation},
  author={John Darby Smith and Aaron J. Hill and Leah Reeder and Brian Claude Franke and Richard B. Lehoucq and Ojas Parekh and William M. Severa and James B. Aimone},
  journal={ArXiv},
  year={2021},
  volume={abs/2107.13057}
}
Computing stands to be radically improved by neuromorphic computing (NMC) approaches inspired by the brain's incredible efficiency and capabilities. Most NMC research, which aims to replicate the brain's computational structure and architecture in man-made hardware, has focused on artificial intelligence; however, less explored is whether this brain-inspired hardware can provide value beyond cognitive tasks. We demonstrate that high-degree parallelism and configurability of spiking neuromorphic… 

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