Design and Implementation of High Speed Artificial Neural Network Based Sprott 94 S System on FPGA

Yıl 2016, , 33 - 39, 27.05.2016

Ismail Koyuncu

https://doi.org/10.18201/ijisae.97824

Cited By: 7

Öz

FPGA-based embedding system designs have been preferred for industrial applications and prototyping because of the advantages of parallel processing, reconfigurability and low cost. Due to having characteristic structure of the parallel processing of Artificial Neural Networks (ANNs), these systems provide the advantage of speed and performance when they are implemented with FPGA-based hardware. The hardware implementation of transfer functions used for modeling non-linear systems is a challenging problem. Therefore, this problem creates convergence problems. In this paper, non-linear Sprott 94 S system has been modeled using ANNs running on FPGA. All related parameter values and processes are defined with IEEE-754-1985 32-bit floating point number format. ANN-based Sprott 94 S system design has been developed using VHDL synthesized using Xilinx ISE Design Tools. In test stage, ANN-based Sprott 94 S system has been tested using 3X100 data set and obtained error analysis results have been presented.  The constructed design has been performed for Xilinx VIRTEX-6 family XC6VHX255T-3FF1923 FPGA chip using Place&Route process and chip usage statistics have been given. The clock frequency of ANN-based Sprott 94 S system which has pipeline processing scheme has been obtained with the value of 304.534 MHz. Accordingly, the proposed FPGA-based ANN system has produced 3X3.284 billion outputs in 1 second.

Anahtar Kelimeler

FPGA, VHDL, Nonlinear Systems, ANN, Sprott 94 S System.

Kaynakça

• H. H. Chiang, K. C. Hsu and I. H. Li (2015). Optimized adaptive motion control through an SoPC implementation for linear induction motor drives. IEEE/ASME Transactions on Mechatronics Vol. 20(1). Pages. 348–360.
• Y. Yue, S. W. Feng, C. S. Guo, X. Yan and R. R Feng (2015). All-digital thermal distribution measurement on field programmable gate array using ring oscillators. Microelectronics Reliability. Vol. 55(2). Pages. 396–401.
• E. Tlelo-Cuautle, V. H. Carbajal-Gomez, P. J. Obeso-Rodelo, J. J. Rangel-Magdaleno and J. C. Nuñez-Perez (2015). FPGA realization of a chaotic communication system applied to image processing. Nonlinear Dynamics. Vol. 82(4). Pages. 1879–1892.
• Ö. Polat and T. Yıldırım (2010). FPGA implementation of a general regression neural network: an embedded pattern classification system. Digital Signal Process. Vol. 20. Pages. 881–886.
• M. Milanovic, M. Truntic, P. Slibar and D. Dolinar (2007). Reconfigurable digital controller for a buck converter based on FPGA. Microelectronics Reliability. Vol. 47(1). Pages. 150–154.
• I. Sahin (2011). A 32-bit floating-point module design for 3D graphic transformations. Scientific Research Essay. Vol. 5(20). Pages. 3070–3081.
• J. X. Wu, C. H. Lin, Y. C. Du, P. J. Chen, C. C. Shih and T. Chen (2010). Estimation of arteriovenous fistula stenosis by FPGA based Doppler flow imaging system. 2015 IEEE International Symp. In Ultrasonics (IUS). Pages. 1–4.
• J. Vanhamel, D. Fussen, E. Dekemper, E. Neefs, B. Van-Opstal, D. Pieroux and P. Leroux (2015). RF-driving of acoustic-optical tunable filters; design, realization and qualification of analog and digital modules for ESA. Microelectronics Reliability. Vol. 55(9). Pages. 2103–2107.
• L. Y. Ann, P, Ehkan and M. Y. Mashor (2016). Possibility of hybrid multilayered perceptron neural network realisation on FPGA and its challenges. In Advanced Computer and Comm. Eng. Tech. Pages. 1051–1061.
• M. T. Hagan, H. B. Demuth and M. Beale (2002). Neural network design. Thomson Learning Press. ISBN-10: 7111108418.
• I. Koyuncu, A. T. Ozcerit and I. Pehlivan (2014). Implementation of FPGA-based real time novel chaotic oscillator. Nonlinear Dynamics. Vol. 77. Pages. 49–59.
• X. Yang, J. Cao and D. W. Ho (2014). Exponential synchronization of discontinuous neural networks with time-varying mixed delays via state feedback and impulsive control. Cognitive Neurodyn. Vol. 9. Pages. 113–128.
• J. Fei and H. Ding (2012). Adaptive sliding mode control of dynamic system using RBF neural network. Nonlinear Dynamics. Vol. 70. Pages. 1563–1573.
• D. Avci, M. K. Leblebicioglu, M. Poyraz and E. Dogantekin (2014). A new method based on adaptive discrete wavelet entropy energy and neural network classifier (ADWEENN) for recognition of urine cells from microscopic images independent of rotation and scaling. Journal Medicial Systems. Vol. 38(2). Pages. 1–9.
• S. L. Ho and Y. Shiyou (2012). A fast robust optimization methodology based on polynomial chaos and evolutionary algorithm for inverse problems. IEEE Transactions on Magnetics. Vol. 48(2). Pages. 259–262.
• C. J. Lin, H. M. Tsai (2008). FPGA implementation of a wavelet neural network with particle swarm optimization learning, Math. & Comp. Modell. Vol. 47. Pages. 982–996.
• O. L. Savkay, V. Tavsanoglu, M. E. Yalcin and E. Cesur (2015). Computer assisted sperm analysis system designed on a hybrid CPU+ FPGA architecture. 23th IEEE Signal Processing and Communications Applications Conference (SIU). Pages. 1425–1428.
• V. Paukštaitis and A. Dosinas (2009). Pulsed neural networks for image processing. International Journal of Electronics and Electrical Eng. Vol. 7. Pages. 15–20.
• H. Papadopoulos and H. Haralambous (2011). Reliable prediction intervals with regression neural networks. Neural Networks. Vol. 24. Pages. 842–851.
• M. Kanayama, A. Rohe and L. A. Paassen (2014). Using and improving neural network models for ground settlement prediction. Geotechnical and Geological Engineering. Vol. 32. Pages. 687–697.
• S. Haykin (1999). Neural networks a comprehensive foundation. Prentice Hall.
• J. C. Sprott (1994). Some simple chaotic flows. Physical Review E. Vol. 50(2). Pages. 647–650.
• Ü. Çavuşoğlu, A. Akgül, S. Kaçar, İ. Pehli̇van and A. Zengi̇n (2016). A novel chaos‐based encryption algorithm over TCP data packet for secure communication. Security and Communication Networks. DOI: 10.1002/sec.1414.
• S. Senthilkumar and A. Piah (2012). An improved fuzzy cellular neural network (IFCNN) for an edge detection based on parallel Runge-Kutta (5, 6) approach. International Journal of Computational Systems Engineering. Vol. 1(1). Pages. 70–78.
• İ. Sahin and İ. Koyuncu (2011). FPGA çipleri için CORDIC Tabanlı exp(x) hesaplama ünitesi tasarımı. e-Journal of New World Sciences Academy. Vol. 6(4). Pages. 1565–1572.