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arXiv · 1802.02627

Going Deeper in Spiking Neural Networks: VGG and Residual Architectures

Abhronil Sengupta·Yuting Ye·Robert Wang·Chiao Liu·Kaushik Roy
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Abstract

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.

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Abhronil Sengupta, Yuting Ye, Robert Wang, Chiao Liu, Kaushik Roy. 2019-02-19. Going Deeper in Spiking Neural Networks: VGG and Residual Architectures. https://arxiv.org/abs/1802.02627

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