Classification of Database by Using Parallelization of Algorithms Third Generation in a GPU

This manuscript is focused on the efficiency analysis of Artificial Neural Networks (ANN) that belongs to the third generation, which are Spiking Neural Networks (SNN) and Support Vector Machine (SVM). The main issue of scientific community have been to improve the efficiency of ANN. So, we applied architecture GPU (Graphical Processing Unit) from NVIDIA model GeForce 9400M. On the other hand, the results of QP method for SVM depends on computational complexity of the algorithm, which is proportional to the volume and attributes of the data. Moreover, SNN was selected because it is a method that has not been explored fully. Despite the economic cost is very high in parallel programming, this is compensated with the large number of real applications such as clustering and pattern recognition. In the state of the art, nobody of authors has coded Quadratic Programming (QP) of SVM in a GPU. In case of SNN, it has been developed by using a specific software as MATLAB, FPGA or sequential circuits but it have never been coded in a GPU. Finally, it is necessary to reduce the grade of parallelization caused by limitations of hardware.

[1]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[2]  V.P. Plagianakos,et al.  Spiking neural network training using evolutionary algorithms , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[3]  Marco Aurelio Nuño-Maganda,et al.  Real-time FPGA-based architecture for bicubic interpolation: an application for digital image scaling , 2005, 2005 International Conference on Reconfigurable Computing and FPGAs (ReConFig'05).

[4]  Nikil D. Dutt,et al.  Efficient simulation of large-scale Spiking Neural Networks using CUDA graphics processors , 2009, 2009 International Joint Conference on Neural Networks.

[5]  Wayne Luk,et al.  FPGA Accelerated Simulation of Biologically Plausible Spiking Neural Networks , 2009, 2009 17th IEEE Symposium on Field Programmable Custom Computing Machines.

[6]  Pietro Laface,et al.  Parallel implementation of artificial neural network training , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[7]  Austin Carpenter,et al.  CUSVM: A CUDA IMPLEMENTATION OF SUPPORT VECTOR CLASSIFICATION AND REGRESSION , 2009 .

[8]  Raghavendra D. Prabhu,et al.  SOMGPU: An unsupervised pattern classifier on Graphical Processing Unit , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[9]  Benjamin Schrauwen,et al.  Improving SpikeProp: Enhancements to An Error-Backpropagation Rule for Spiking Neural Networks , 2004 .

[10]  John R. Williams,et al.  Parallel multiclass classification using SVMs on GPUs , 2010, GPGPU-3.

[11]  Sander M. Bohte,et al.  Error-backpropagation in temporally encoded networks of spiking neurons , 2000, Neurocomputing.

[12]  Qi Li,et al.  An intelligent system for accelerating parallel SVM classification problems on large datasets using GPU , 2010, 2010 10th International Conference on Intelligent Systems Design and Applications.

[13]  A. Hodgkin,et al.  A quantitative description of membrane current and its application to conduction and excitation in nerve , 1952, The Journal of physiology.

[14]  Eugene M. Izhikevich,et al.  Simple model of spiking neurons , 2003, IEEE Trans. Neural Networks.

[15]  Shao-Yi Chien,et al.  Support Vector Machines on GPU with Sparse Matrix Format , 2010, 2010 Ninth International Conference on Machine Learning and Applications.

[16]  Ulrich Brunsmann,et al.  FPGA-GPU architecture for kernel SVM pedestrian detection , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.

[17]  Wyeth Bair,et al.  Spiking neural network simulation: numerical integration with the Parker-Sochacki method , 2009, Journal of Computational Neuroscience.

[18]  Murray Shanahan,et al.  NeMo: A Platform for Neural Modelling of Spiking Neurons Using GPUs , 2009, 2009 20th IEEE International Conference on Application-specific Systems, Architectures and Processors.

[19]  Laurene V. Fausett,et al.  Fundamentals Of Neural Networks , 1994 .

[20]  Vivek K. Pallipuram,et al.  Acceleration of spiking neural networks in emerging multi-core and GPU architectures , 2010, 2010 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW).

[21]  Ramón Huerta,et al.  Self-organization in the olfactory system: one shot odor recognition in insects , 2005, Biological Cybernetics.

[22]  Kurt Keutzer,et al.  Fast support vector machine training and classification on graphics processors , 2008, ICML '08.

[23]  Christos-Savvas Bouganis,et al.  Performance comparison of GPU and FPGA architectures for the SVM training problem , 2009, 2009 International Conference on Field-Programmable Technology.

[24]  Eugene M Izhikevich,et al.  Hybrid spiking models , 2010, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[25]  Hojjat Adeli,et al.  Spiking Neural Networks , 2009, Int. J. Neural Syst..

[26]  Leon Reznik,et al.  GPU-based simulation of spiking neural networks with real-time performance & high accuracy , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).