Analysis of a GPU based CNN implementation

The CNN (Cellular Neural Network) is a powerful image processing architecture whose hardware implementation is extremely fast. The lack of such hardware device in a development process can be substituted by using an efficient simulator implementation. Commercially available graphics cards with high computing capabilities make this simulator feasible. The aim of this work is to present a GPU based implementation of a CNN simulator using nVidia's Fermi architecture. Different implementation approaches are considered and compared to a multi-core, multi-threaded CPU and some earlier GPU implementations. A detailed analysis of the introduced GPU implementation is presented.