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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 644266260
doi: 10.4316/AECE


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ABC Algorithm based Fuzzy Modeling of Optical Glucose Detection, SARACOGLU, O. G., BAGIS, A., KONAR, M., TABARU, T. E.
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2016-Jun-14
Thomson Reuters published the Journal Citations Report for 2015. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.459, and the JCR 5-Year Impact Factor is 0.442.

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  1/2015 - 5

An Evolutionary Approach to the Soft Error Mitigation Technique for Cell-Based Design

PARK, J. K. See more information about PARK, J. K. on SCOPUS See more information about PARK, J. K. on IEEExplore See more information about PARK, J. K. on Web of Science, KIM, J. T. See more information about KIM, J. T. on SCOPUS See more information about KIM, J. T. on SCOPUS See more information about KIM, J. T. on Web of Science
 
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Download PDF pdficon (789 KB) | Citation | Downloads: 236 | Views: 928

Author keywords
soft error mitigation, single event transient, cell-based design, cell sizing, hybrid genetic algorithm

References keywords
soft(16), error(13), systems(8), design(8), analysis(8), rate(7), designs(7), circuits(6), logic(5), level(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2015-02-28
Volume 15, Issue 1, Year 2015, On page(s): 33 - 40
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2015.01005
Web of Science Accession Number: 000352158600005
SCOPUS ID: 84924812869

Abstract
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In this paper, we present a soft error mitigation algorithm that searches for the proper gate sizes within constrained gate-level designs. The individual gate sizing has an impact on the former optimization results and degrades the quality of the solution. In order to address this inefficiency, we utilize a modified topological sort that preserves the preceding local optima. Using a new local searcher, a hybrid genetic optimization technique for soft error mitigation is proposed. This evolutionary search algorithm has general genetic operators: the initialization of the population, crossover, mutation and selection operators. The local searcher consists of two subsequent heuristics. These search algorithms make the individual chromosome move to better search regions in a short time and then, the population acquires various candidates for the global optimum with the help of other genetic operators. The experiments show that the proposed genetic algorithm achieves an approximately 90% reduction in the number of soft errors when compared to the conventional greedy approach with at most 30% overhead for the area and critical path delay.


References | Cited By  «-- Click to see who has cited this paper

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[CrossRef] [Web of Science Times Cited 19] [SCOPUS Times Cited 25]


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[CrossRef] [SCOPUS Times Cited 14]


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[CrossRef] [SCOPUS Times Cited 6]


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[CrossRef] [Web of Science Times Cited 541] [SCOPUS Times Cited 694]


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[CrossRef] [Web of Science Times Cited 51] [SCOPUS Times Cited 76]


[6] R. R. Rao, K. Chopra, and D. T. Blaauw, "Computing the Soft Error Rate of a Combinational Logic Circuit Using Parameterized Descriptors," IEEE Trans. on Computer-Aided Design of Integrated Circuits and Systems, vol. 26, no. 3, pp. 468-479, 2007.
[CrossRef] [Web of Science Times Cited 44] [SCOPUS Times Cited 64]


[7] J. K. Park, H. S. Choi and J. T. Kim, "A Soft Error Analysis Tool for High-Speed Digital Designs," Proc. of 2nd Int. conf. on Ubiquitous Information Management and Communication, pp. 280-282, 2008.
[CrossRef] [SCOPUS Times Cited 1]


[8] S. Kwon, J. K. Park and J. T. Kim, "An approximated soft error analysis technique for gate-level designs," IEICE Electronics Express, vol. 11, no. 10, pp. 1-7, 2014.
[CrossRef] [Web of Science Record] [SCOPUS Times Cited 1]


[9] H-K. Peng, C. H-P. Wen and B. Jayanta, "On soft error rate analysis of scaled CMOS designs: a statistical perspective," Proc. of IEEE/ACM Int’l Conf. on Computer-Aided Design, pp. 157-163, 2009.
[CrossRef]


[10] Y-H. Kuo, H-K. Peng and C. H-P. Wen, "Accurate statistical soft error rate (SSER) analysis using a quasi-Monte Carlo framework with quality cell models." Proc. of 11th Int’l symposium on Quality Electronic Design (ISQED), pp. 831-838, 2010.
[CrossRef] [SCOPUS Times Cited 9]


[11] A. C-C. Chang, R. H-M. Huang and C. H-P. Wen, "CASSER: A Closed-Form Analysis Framework for Statistical Soft Error Rate," IEEE Trans. on Very Large Scale Integration, vol. 21, no. 10, pp. 1837-1848, 2013.
[CrossRef] [Web of Science Times Cited 11] [SCOPUS Times Cited 11]


[12] N. M. Zivanov and D. Marculescu, "MARS-C: Modeling and Reduction of Soft Errors in Combinational Circuits," Proc. of Design Automation Conference, pp. 767-772, 2006.
[CrossRef]


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[CrossRef] [Web of Science Times Cited 12]


[14] H. Asadi and M. Tahoori, "Soft error hardening for logic-level designs," Proc. of IEEE Symp. on In Circuits and Systems, 2006.
[CrossRef]


[15] Y. S. Dhillon, A. U. Diril, A. Chatterjee, and A. D. Singh, "Analysis and Optimization of Nanometer CMOS Circuits for Soft-Error Tolerance," IEEE Trans. on Very Large Scale Integration Systems, vol. 14, no. 5, pp. 514-524, 2006.
[CrossRef] [Web of Science Times Cited 43] [SCOPUS Times Cited 58]


[16] J. K. Park and J. T. Kim, "A soft error mitigation technique for constrained gate-level designs," IEICE Electronics Express, vol. 5, no. 18, pp. 698-704, 2008.
[CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 7]


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[CrossRef] [Full Text] [Web of Science Times Cited 12] [SCOPUS Times Cited 1]


[18] M. Gen and R. Cheng, "Genetic Algorithms and Engineering Optimization", pp.1-39, John Wiley & Sons, 2000.

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[CrossRef]


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[22] B. Zhang, W. Wang, and M. Orshansky, "FASER: Fast Analysis of Soft Error Susceptibility for Cell-Based Designs," Proc. of 7th Int. Symp. on Quality Electronic Design, pp. 755-760, 2006.
[CrossRef] [SCOPUS Times Cited 79]




References Weight

Web of Science® Citations for all references: 991 TCR
SCOPUS® Citations for all references: 1,373 TCR

Web of Science® Average Citations per reference: 43 ACR
SCOPUS® Average Citations per reference: 60 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2016-12-04 15:41 in 110 seconds.




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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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