Design and implementation of SVM OTPC searching based on Shared Dot Product Matrix

Abstract In this paper, we proposed a FPGA implementation architecture for SVM classifier. The architecture is based on the proposed Shared Dot Product Matrix (SDPM) method which computes and stores the dot product of all training data before SVM searching process. We implemented the proposed method by software simulation and hardware implementation. The software simulation of SDPM method achieves twice the speed of LIBSVM, which is one of the most popular SVM implementation libraries. This acceleration mainly results from the reduction of repeat Kernel function calculation. Then the hardware software collaboration architecture for SDPM is also proposed in this paper. Results show that the proposed architecture achieves approximately 30 times faster searching speed compared with LIBSVM.

[1]  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.

[2]  Srihari Cadambi,et al.  A Massively Parallel FPGA-Based Coprocessor for Support Vector Machines , 2009, 2009 17th IEEE Symposium on Field Programmable Custom Computing Machines.

[3]  Christian Igel,et al.  Maximum-Gain Working Set Selection for SVMs , 2006, J. Mach. Learn. Res..

[4]  Christos-Savvas Bouganis,et al.  A scalable FPGA architecture for non-linear SVM training , 2008, 2008 International Conference on Field-Programmable Technology.

[5]  Chih-Jen Lin,et al.  Working Set Selection Using Second Order Information for Training Support Vector Machines , 2005, J. Mach. Learn. Res..

[6]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[7]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[8]  V. Vapnik.  Estimation of Dependences Based on Empirical Data , 2006 .

[9]  Shawn Martin.  Training support vector machines using Gilbert's algorithm , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[10]  Thorsten Joachims,et al.  Transductive Inference for Text Classification using Support Vector Machines , 1999, ICML.

[11]  S. A. Samad,et al.  Reduced set support vector machines: Application for 2-dimensional datasets , 2008, 2008 2nd International Conference on Signal Processing and Communication Systems.

[12]  Ta-Wen Kuan,et al.  Hardware/software co-design for fast-trainable speaker identification system based on SMO , 2011, 2011 IEEE International Conference on Systems, Man, and Cybernetics.

[13]  Myung-Gil Jang,et al.  A Modified Fixed‐Threshold SMO for 1‐Slack Structural SVMs , 2010 .

[14]  John C. Platt,et al.  Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .

[15]  A. Amonsin,et al.  Serological evidence of pig-to-human influenza virus transmission on Thai swine farms. , 2011, Veterinary microbiology.

[16]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[17]  S. Sathiya Keerthi,et al.  Improvements to Platt's SMO Algorithm for SVM Classifier Design , 2001, Neural Computation.

[18]  Saso Dzeroski,et al.  An extensive experimental comparison of methods for multi-label learning , 2012, Pattern Recognit..

[19]  Mayank Budhiraja.  Multi label text classification for un-trained data through supervised learning , 2017, 2017 International Conference on Intelligent Computing and Control (I2C2).

[20]  David R. Musicant,et al.  Successive overrelaxation for support vector machines , 1999, IEEE Trans. Neural Networks.

[21]  Alexander J. Smola,et al.  Learning with Kernels: support vector machines, regularization, optimization, and beyond , 2001, Adaptive computation and machine learning series.