Going Deeper in Spiking Neural Networks: VGG and Residual Architectures

Understand

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.