A DTCNN universal machine based on highly parallel 2-D cellular automata CAM2

The discrete-time cellular neural network (DTCNN) is a promising computer paradigm that fuses artificial neural networks with the concept of cellular automaton (CA) and has many applications to pixel-level image processing. Although some architectures have been proposed for processing DTCNN, there are no compact, practical computers that can process real-world images of several hundred thousand pixels at video rates. So, in spite of its great potential, DTCNNs are not being used for image processing outside the laboratory. This paper proposes a DTCNN processing method based on a highly parallel two-dimensional (2-D) cellular automata called CAM². CAM² can attain pixel-order parallelism on a single PC board because it is composed of a content addressable memory (CAM), which makes it possible to embed enormous numbers of processing elements, corresponding to CA cells, onto one VLSI chip. A new mapping method utilizes maskable search and parallel and partial write commands of CAM² to enable high-performance DTCNN processing. Evaluation results show that, on average, CAM² can perform one transition for various DTCNN templates in about 12 microseconds. Also it can perform practical image processing through a combination of DTCNNs and other CA-based algorithms. CAM² is a promising platform for processing DTCNN