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.
翻译:过去几年来,Spiking神经网络(SNN)作为使低功率事件驱动神经形态硬件成为可能的通路而成为流行。然而,在机器学习中的应用基本上局限于非常浅的神经网络结构,解决简单的问题。在本文中,我们提出一种新的算法技术,以产生一个具有深层结构的SNN,并展示其在复杂视觉识别问题上的有效性,如CIFAR-10和图像网络。我们的技术适用于VGG和残余网络结构,其精度大大高于最新技术。最后,我们提出对稀有事件驱动计算的分析,以显示在喷射域运行时硬件成本的降低。