CNN-based language and interpreter for image processing on GPUs

Abstract

The inherent massive parallelism of cellular neural networks (CNNs) makes them an ideal computational platform for kernel-based algorithms and image processing. General-purpose graphics processing units (GPUs) provide similar massive parallelism, but it can be difficult to design algorithms to make optimal use of the hardware. In this paper, we present a programming environment based on CNNs that can run on GPUs, multi-core CPUs or simply simulated in software. The platform offers a simplified view of massively parallel computation which remains universal and reasonably efficient. An image processing library with visualisation software has been developed using the abstraction to showcase the flexibility and power of cellular computation on GPUs. A simple virtual machine and language is presented to manipulate images using the library for single-core, multi-core and GPU backends.