Application of Cellular Neural Networks in Semantic Segmentation

The popularity of convolutional neural networks and deep learning based approaches has increased continuously in the past years. These methods has enabled the solution of various practical problems, but they still are not heavily exploited in the embedded domain, which requires low-power implementation of these architectures. Cellular neural networks can provide an analogue and power-efficient implementation of these networks which also enables the exploitation of non-Boolean, beyond CMOS elements such as memristive structures. In this paper we will demonstrate on simple datasets how cellular neural networks with different cell dynamics can be efficiently applied in the problem of semantic segmentation.