Going Deeper in Spiking Neural Networks: VGG and Residual Architectures

@article{Sengupta2019GoingDI,
  title={Going Deeper in Spiking Neural Networks: VGG and Residual Architectures},
  author={A. Sengupta and Yuting Ye and Robert Y. Wang and C. Liu and K. Roy},
  journal={Frontiers in Neuroscience},
  year={2019},
  volume={13}
}
  • A. Sengupta, Yuting Ye, +2 authors K. Roy
  • Published 2019
  • Computer Science, Psychology, Medicine
  • Frontiers in Neuroscience
  • Over the past few years, Spiking Neural Networks (SNNs) have become popular as a possible pathway to enable low-power event-driven neuromorphic hardware. [...] Key Method Our technique applies to both VGG and Residual network architectures, with significantly better accuracy than the state-of-the-art. Finally, we present analysis of the sparse event-driven computations to demonstrate reduced hardware overhead when operating in the spiking domain.Expand Abstract
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