ConvNets Architecture for Complex Mixed Analogue-Digital Simulations

The Convolutional Neural Networks (ConvNets) with its proper hierarchical structure are known as powerful image-recognition processing architecture. In particular, the ConvNets are well suited for several image processing tasks, such as image classification data set, computer vision and natural language processing. Nevertheless, the implementation of ConvNets requires a large amount of operations as the 2-D convolutional mappings that need a very large computational power. In this paper, the authors will investigate alternative hardware architectures, based on Cellular Neural Networks (CeNNs), in order to improve the overall performances.