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CNN Virtual Machines(a)

by Ryanne Dolan

Cellular Neural Network Virtual Machine for Graphics Hardware with Applications in Image Processing

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 computation using cellular neural networks. The abstraction offers a simplified view of massively parallel computation which remains universal and reasonably efficient. An image processing library with visualization 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 back-ends.

References

  1. R. T. Dolan and G. N. DeSouza, " Using GPUs to Create a CNN-based Platform for Image Processing",  Int. Journal of Parallel, Emergent, and Distributed Systems (JPEDS) (accepted).
     

  2. Dolan, R., DeSouza, G. N., and Caputo, D., " The Swarm Computer: an Analog Cellular-Swarm Hybrid Architecture", 2010 IEEE 10th Intl. Conference on Hybrid Intelligent Systems, Atlanta, USA.
     

  3. Dolan, R. T and DeSouza, G. N., "GPU-Based Simulation of Cellular Neural Networks for Image Processing", in the Proceedings of the 2009 IEEE International Joint Conference on Neural Networks (IJCNN), pp. 2712-2717, June 14-19, Atlanta, Georgia.

(a) This research made use of GTX480s and Tesla's S1070 donated by NVIDIA via their Academic Partnership Program.

 

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