FPGA Implementation of SVM Decision Function Based on Hardware-Friendly Kernel

We present a design scheme for SVM decision function based on the hardware-friendly kernel on FPGA device. This scheme is suitable for classification and regression problems. We adopt ModelSim simulation platform for SVM classification and regression experiments. The hardware implementation obtains the same classification accuracy as the LIBSVM package by using the appropriate fixed-point number precision in classification experiments. We had done the preliminary study on the precision of input parameters in SVC by choosing fixed-point arithmetic; and the minimum number of bits of SVR input parameters was obtained in the case of not reducing the performance of SVM classifier. The mean square error of the hardware implementation is less than 0.004 in regression experiments, with good regression performance.

[1]  Davide Anguita,et al.  A support vector machine with integer parameters , 2008, Neurocomputing.

[2]  B. Venkataramani,et al.  FPGA Implementation of Support Vector Machine Based Isolated Digit Recognition System , 2009, 2009 22nd International Conference on VLSI Design.

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

[4]  Davide Anguita,et al.  A digital architecture for support vector machines: theory, algorithm, and FPGA implementation , 2003, IEEE Trans. Neural Networks.

[5]  Raquel Valdés-Cristerna,et al.  An FPGA Implementation of Linear Kernel Support Vector Machines , 2006, 2006 IEEE International Conference on Reconfigurable Computing and FPGA's (ReConFig 2006).

[6]  Marta Ruiz-Llata,et al.  FPGA Implementation of Support Vector Machines for 3D Object Identification , 2009, ICANN.

[7]  William M. Pottenger,et al.  Finite precision analysis of support vector machine classification in logarithmic number systems , 2004, Euromicro Symposium on Digital System Design, 2004. DSD 2004..

[8]  Sung Bum Pan,et al.  SVM-Based Speaker Verification System for Match-on-Card and Its Hardware Implementation , 2006 .

[9]  Stamatis Vassiliadis,et al.  A sum of absolute differences implementation in FPGA hardware , 2002, Proceedings. 28th Euromicro Conference.

[10]  Davide Anguita,et al.  Feed-Forward Support Vector Machine Without Multipliers , 2006, IEEE Transactions on Neural Networks.

[11]  Theocharis Theocharides,et al.  SCoPE: Towards a Systolic Array for SVM Object Detection , 2009, IEEE Embedded Systems Letters.

[12]  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.

[13]  Davide Anguita,et al.  The Digital Kernel Perceptron , 2002 .