A novel mechanism for converting convolutional neural networks (CNNs) to spiking neural networks (SNNs) to facilitate ready deployment, that is, mapping on spiking hardware architectures, is proposed in this paper. The authors have provided a detailed analysis of CNNs and their applications whilst highlighting the deep learning nature of such neural networks. Fundamentally, CNNs have been mapped on conventional central processing units (CPUs) with numerical processing capabilities. However, modern-day versions of CNNs with increasing complexities demand high-performance processing capability, which essentially limits their ready adoption.
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