dc.contributor.author | Dolan, Ryanne | eng |
dc.contributor.author | DeSouza, Guilherme | eng |
dc.date.issued | 2009-06 | eng |
dc.description.abstract | The inherent massive parallelism of cellular neural networks makes them an ideal computational platform for kernelbased
algorithms and image processing. General-purpose GPUs provide similar massive parallelism, but it can be difficult to
design algorithms to make optimal use of the hardware. The presented research includes a GPU abstraction based on cellular neural networks. The abstraction offers a simplified view of massively parallel computation which remains reasonably efficient. An image processing library with visualization software has been developed to showcase the flexibility and power of cellular computation on GPUs. Benchmarks of the library indicate that
commodity GPUs can be used to significantly accelerate CNN research and offer a viable alternative to CPU-based image processing algorithms. | eng |
dc.identifier.citation | Proceedings of International Joint Conference on Neural Networks, Atlanta, Georgia, USA, June 14-19, 2009. | eng |
dc.identifier.isbn | 978-1-4244-3553-1/09 | eng |
dc.identifier.uri | http://hdl.handle.net/10355/9206 | eng |
dc.language | English | eng |
dc.publisher | IEEE | eng |
dc.relation.ispartof | Electrical and Computer Engineering publications (MU) | eng |
dc.relation.ispartofcommunity | University of Missouri-Columbia. College of Engineering. Department of Electrical and Computer Engineering | eng |
dc.rights | OpenAccess. | eng |
dc.rights.license | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License. | eng |
dc.subject | parallel image processing | eng |
dc.subject | graphics processing units | eng |
dc.subject.lcsh | Neural networks (Computer science) | eng |
dc.subject.lcsh | Graphics processing units | eng |
dc.subject.lcsh | Image processing | eng |
dc.title | GPU-Based Simulation of Cellular Neural Networks for Image Processing | eng |
dc.type | Article | eng |