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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Parallel, Emergent and Distributed Systems
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
