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

Abstract

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 single precision floatingpoint number standard. 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.

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