An exploration of digital CNN implementations

Computationally hard problems, such as operations on n-dimensional maps, are in need of efficient solutions. Cellular Neural Networks have this promise. This paper explores digital realizations of such computational paradigms through the case of real-time image processing. It is shown that a behaviour-based spatial unrolling of the time-critical numerical convergence loop outperforms a classical function-based architecture. An FPGA implementation on Xilinx Virtex-II illustrates a 50 times higher throughput, bringing intelligent vision into reach