Over the past few years, Spiking Neural Networks (SNNs) have become popular as a possible pathway to enable low-power event-driven neuromorphic hardware. However, their application in machine learning have largely been limited to very shallow neural network architectures for simple problems. In this paper, we propose a novel algorithmic technique for generating an SNN with a deep architecture, and demonstrate its effectiveness on complex visual recognition problems such as CIFAR-10 and ImageNet. 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.
@article{arxiv.1802.02627,
title = {Going Deeper in Spiking Neural Networks: VGG and Residual Architectures},
author = {Abhronil Sengupta and Yuting Ye and Robert Wang and Chiao Liu and Kaushik Roy},
journal= {arXiv preprint arXiv:1802.02627},
year = {2019}
}