GPU-based simulation of cellular neural networks for image processing

Authors:
Ryanne Dolan;Guilherme DeSouza
Affiliations:
Vision-Guided and Intelligent Robotics Laboratory, University of Missouri, Columbia, MO;Vision-Guided and Intelligent Robotics Laboratory, University of Missouri, Columbia, MO
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

Citing 4
Cited 0

Multi-Valued and Universal Binary Neurons: Theory, Learning and Applications

Multi-Valued and Universal Binary Neurons: Theory, Learning and Applications
Universality and Emergent Computation in Cellular Neural Networks (World Scientific Series on Nonlinear Science, Series a, 43)

Universality and Emergent Computation in Cellular Neural Networks (World Scientific Series on Nonlinear Science, Series a, 43)
GPU Computing: Programming a Massively Parallel Processor

Proceedings of the International Symposium on Code Generation and Optimization
Parallelization of cellular neural networks on GPU

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Abstract

The inherent massive parallelism of cellular neural networks makes them an ideal computational platform for kernel-based 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.