SOMGPU: An unsupervised pattern classifier on Graphical Processing Unit

Abstract

Graphical Processing Units (GPUs) have been, lately used for general purpose tasks owing to their implicit parallel nature. One such task is that of pattern classification. Highly parallel tasks like these suffer from performance loss owing to the sequential nature of Central Processing Unit (CPU). To match the image processing power of human brain even slightly, this problem beckons the utilization of enormous computational power and parallel environs of GPUs. Unless there is a task which can be parallelized to the required extent the gain obtained is lost owing to the overhead involved. Thus, it is equally important to understand some limitations of GPU before venturing in this direction and deal with it appropriately to obtain satisfactory results. Artificial Neural Networks (ANN) are found to be appropriate while dealing with pattern recognition problems. Kohonen's Self Organizing Map (SOM) has been used for classification out of other approaches for its implicit parallel nature, albeit with minor modifications to make it suit the parallel environment. nVIDIA GeForce 6150 Go with Microsoft Research Accelerator as the high level library has been chosen as the platform to provide this environment. � 2008 IEEE.