FPGAs as a digital design based platforms, are usually candidates over DSP processors and many other digital hardware platforms for real time systems. FPGAs products for different companies have various number of available resources. So, the numbers of configurable logic blocks and input / output ports are varied for different versions. The wrong choice for the suitable FPGA plant for specific applications leads to do not have full utilities from the used FPGA processor, and may use only less than 10% from its resources that make the designer lose more than 90% of the processor utilities. In this paper, intelligent systems is built to determine the most suitable Xilinx processor depending on its available resources and other specific criteria, such as the operation speed of real time processing. The intelligent system can also choose the suitable partitioning algorithms and segmentation techniques for processing of specific applications with high data density. The suggested intelligent system built on an extended data base of different FPGA processors with comprehensive information about available resources of each version with detail information about frequency, processing speed and many other specific features of each processor. This intelligent system must include a wide experience of different digital technique and adapting algorithms as well as partitioning and segmentation criteria. Thus, an intelligent system that supported by this wide experiences and activities can provide a perfect decision to avoid any lose for processor components and utilities by efficiently determining the suitable FPGA processor for specific applications. As well as by choosing more efficient data processing technique. The suggested intelligent FPGA based system is built using Xilinx System Generator block sets, and Xilinx software of ISE 14.7 cooperated with MATLAB R2013b. The used Xilinx processors in this paper are Spartan, Artix, Kintex, and Virtex processors
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
- A. H. Rasheed, “FPGA Design and Implementation of Huge Data Systems”, IJCA, Volume 174 – No.7, September 2017
- R. J. Petersen and B. L. Hutchings, “An assessment of the suitability of FPGA-based systems for use in digital signal processing,” Conference on Future Generation Computer Systems, vol. 22, pp. 67–79, Jan 2008.
- Ownby,M., Mahmoud,W.H., “A design methodology for implementing DSP with Xilinx System Generator for Matlab,” IEEE International Symposium on System Theory, pp.404-408, 2003
- N. Vani, N. Usha rani “FPGA implementation of image filtering applications using XGS”, International Conference on Navigational Systems & Signal Processing Applications, December 13th &14th, 2013.
- Xilinx, Sparten 3 FPGA Family: Complete Data Sheet. www.xilinx.com.
- A. H. Rasheed, “FPGA-based Optimized Systolic Design for Median Filtering Algorithms”, IJAER, Vol. 12, No. 24 (2017) pp. 16100-16113.
- Vadim Belov, Sergey Mosin, " FPGA Implementation of LTE Turbo Decoder Using MAX- log MAP Algorithm", in 2017 IEEE Embedded Computing.
- G. Santosh, S. Rajaram , “Design And Implementation Of Turbo Coder For LTE on FPGA”, IJESS, Vol. 4 : Iss. 1 , Article 7.
- S. Gaur, S. Sharma, V.K Gupta, "Implementation of ANC System Using Xilinx System Generator (Co-hardware Simulation using Vertex 6 FPGA Kit)" 2015 IJEDR, Volume 3, Issue 2, ISSN: 2321-9939.
- Xilinx, Using Block RAM in Spartan-3 FPGAs, Dual-Port Block Memory Core v6.3,. www.xilinx.com.
- B. Stellato, T. Geyer, and P. J. Goulart, "High-Speed Finite Control Set Model Predictive Control for Power Electronics," IEEE Trans. Power Electron., vol. 32, pp. 4007-4020, 2017.
- P. M. Sanchez, O. Machado, E. J. B. Peña, F. J. Rodriguez, and F. J. Meca, “FPGA-based implementation of a predictive current controller for power converters,” IEEE Trans. Ind. Informat., vol. 9, no. 3, pp. 1312-1321, Aug. 2013.