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A HW/SW CO-VERIFICATION METHOD FOR ASK USING FPGA TEST

M. Siva Kumar, T. C.Sanjeeva Rayudu, Vempalle Rafi, M. Rajesh

Abstract


Field programmable gate arrays (FPGAs) may be used in a wide variety of settings. If weak points in an FPGA can be isolated, then the device's shortcomings may be endured with relative ease. The research provides a recommendation and unveils a hardware/software co-verification approach for testing FPGAs. Using the adaptability and visibility of software in combination with large-speed simulation of the hardware, this process can do comprehensive, automated testing of every input/output block (IOB) and custom configurable logic block (CLB) of an FPGA. The proposed technique may detect faulty cells in an FPGA mechanically. Therefore, test efficiency and reliability may be enhanced without the devoir of physical work. A hardware-software co-verification network consists of a software simulator and hardware emulation, with the PCI bus acting as the connection between the two. To speed up SOC verification, the hardware emulator maps certain target design modules while the software simulator mimics others. The standard FPGA test strategy's higher costs and requirement for individual PCBs for each FPGA may be avoided.


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References


S.Toutounchi, A. Lai, FPGA Test and Coverage, ITC International Test Conference, 2002, 599-607.

Huang W K, Meyer F J, Lombardi F. Multiple fault detection in logic resources of FPGAs. Proceedings IEEE International Symposium on Defect and Fault Tolerance in VLSI Systems, 1997,186-194.

Gevers, T. “Robust segmentation and tracking of color objects in video, Circuits and Systems for Video Technology” IEEE Transactions on, Volume 14, Issue 6, Jun 2004. pp:776 - 781.

J. F. Haddon, “Generalized threshold selection for edge detectionPattern Recognition.”, vol. 21, pp. 195-203.

J. Shanthi, M. Sasi Kumar and C. Kesavdas “Segmentation of Brain MRI and Comparison Using Different Approaches of 2D Seed Growing” Book: 13th international conference on Biomedical engineering, Springer Vol.23 pp 35-38Y.

Gevers, T. “Robust segmentation and tracking of color objects in video,Circuits and Systems for Video Technology” IEEE Transactions on, Volume 14, Issue 6, Jun 2004. pp:776 - 781.M. Young, The Technical Writer’s Handbook. Mill Valley, CA: University Science, 1989.

R J Almeida and JMC Sousa “Comparison of fuzzy clustering algorithms for classification, International symposium on evolving fuzzy Systems”, September 2006.

Kang, Wen-Xiong; Yang, Qing-Qiang; Liang, Run-Peng;” The Comparative Research on Image Segmentation Algorithms, Education Technology and Computer Science, 2009. ETCS ’09. First International Workshop on Volume 2, 7-8 March 2009 pp 703 - 707.


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