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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
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  4/2013 - 15

Improved Low Power FPGA Binding of Datapaths from Data Flow Graphs with NSGA II -based Schedule Selection

RAM, D. S. H. See more information about RAM, D. S. H. on SCOPUS See more information about RAM, D. S. H. on IEEExplore See more information about RAM, D. S. H. on Web of Science, BHUVANESWARI, M. C. See more information about  BHUVANESWARI, M. C. on SCOPUS See more information about  BHUVANESWARI, M. C. on SCOPUS See more information about BHUVANESWARI, M. C. on Web of Science, UMADEVI, S. See more information about UMADEVI, S. on SCOPUS See more information about UMADEVI, S. on SCOPUS See more information about UMADEVI, S. on Web of Science
 
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Download PDF pdficon (613 KB) | Citation | Downloads: 382 | Views: 1,535

Author keywords
high level synthesis, field programmable gate arrays, power dissipation, genetic algorithms, reconfigurable logic

References keywords
power(11), design(10), vlsi(9), systems(8), optimization(7), level(7), high(7), integration(5), evolutionary(5), very(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2013-11-30
Volume 13, Issue 4, Year 2013, On page(s): 85 - 92
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2013.04015
Web of Science Accession Number: 000331461300015
SCOPUS ID: 84890198448

Abstract
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FPGAs are increasingly being used to implement data path intensive algorithms for signal processing and image processing applications. In High Level Synthesis of Data Flow Graphs targeted at FPGAs, the effect of interconnect resources such as multiplexers must be considered since they contribute significantly to the area and switching power. We propose a binding framework for behavioral synthesis of Data Flow Graphs (DFGs) onto FPGA targets with power reduction as the main criterion. The technique uses a multi-objective GA, NSGA II for design space exploration to identify schedules that have the potential to yield low-power bindings from a population of non-dominated solutions. A greedy constructive binding technique reported in the literature is adapted for interconnect minimization. The binding is further subjected to a perturbation process by altering the register and multiplexer assignments. Results obtained on standard DFG benchmarks indicate that our technique yields better power aware bindings than the constructive binding approach with little or no area overhead.


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

[1] D. S. H. Ram, M. C. Bhuvaneswari, S. M. Logesh, "A novel evolutionary technique for multi-objective power, area and delay optimization in high level synthesis of datapaths," IEEE Computer Society Annual Symposium on VLSI, ISVLSI, pp.290-295, 2011
[CrossRef] [Web of Science Times Cited 4] [SCOPUS Times Cited 2]


[2] D. S. Harish Ram, M. C. Bhuvaneswari, Shanthi S. Prabhu, "A novel framework for applying multiobjective GA and PSO based approaches for simultaneous area, delay, and power optimization in high level synthesis of datapaths, VLSI Design journal, vol 2012.
[CrossRef] [SCOPUS Times Cited 4]


[3] K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, "A fast and elitist multiobjective genetic algorithm: NSGA-II," IEEE Transactions on Evolutionary Computation, vol.6, no.2, pp.182-197, 2002
[CrossRef] [Web of Science Times Cited 8531] [SCOPUS Times Cited 12903]


[4] K. Deb, "Multi-objective optimization using evolutionary algorithms," John Wiley and Sons, 2003

[5] V. Krishnan, S. Katkoori, A genetic algorithm for the design space exploration of datapaths during high-level Synthesis," IEEE Transactions on Evolutionary Computation, vol.10, no.3, pp. 213- 229 2006
[CrossRef] [Web of Science Times Cited 29] [SCOPUS Times Cited 64]


[6] E. Casseau, B. Le Gal, "High-level synthesis for the design of FPGA-based signal processing systems," International Symposium on Systems, Architectures, Modeling and Simulation, SAMOS'09, pp.25-32, 2009

[7] C. Y. Huang, Y. S. Chen, Y. L. Lin, Y. C. Hsu, "Data path allocation based on bipartite weighted matching," Proceedings of the 27th ACM/IEEE Design Automation Conference, DAC '90, pp 499-504, 1990
[CrossRef]


[8] Chittaranjan A. Mandal, P. P. Chakrabarti, Sujoy Ghose, "GABIND: A GA approach to allocation and binding for the high-level synthesis of data paths," Very Large Scale Integration (VLSI) Systems, vol 8 no. 6, pp 747-750, 2000

[9] X. Tang, T. Jiang, A. Jones, and P. Banerjee, "Behavioral synthesis of data-dominated circuits for minimal energy implementation," Proceedings of the International Conference on VLSI Design, pp 267-273, 2005.

[10] N. Chabini, W. Wolf, "Unification of scheduling, binding and retiming to reduce power consumption under timings and resources constraints," Very Large Scale Integration (VLSI) Systems, vol.13, no. 10, pp. 1113-1126, 2005.

[11] A. K. Murugavel, N. Ranganathan, "A game theoretic approach for power optimization during behavioral synthesis," IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 11, no. 6, pp. 1031-1043, 2003.
[CrossRef] [Web of Science Times Cited 11] [SCOPUS Times Cited 13]


[12] D. Chen, J. Cong, Y. Fan, L. Wan, "LOPASS: A Low-power architectural synthesis system for FPGAs with interconnect estimation and optimization," IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol.18, no.4, pp.564-577, 2010
[CrossRef] [Web of Science Times Cited 8] [SCOPUS Times Cited 18]


[13] A. A. Del Barrio, S. O. Memik, M. C. Molina, J. M. Mendias, R. Hermida, "A fragmentation aware high-level synthesis flow for low power heterogeneous datapaths," Integration, the VLSI journal,
[CrossRef] [Web of Science Times Cited 1] [SCOPUS Record]


[14] D. Bekiaris, E. S. Xanthopoulos, G. Economakos, D. Soudris, "Systematic design and evaluation of a scalable reconfigurable multiplier scheme for HLS environments," International workshop on communication centric system on chip ReCoSoC, pp 1-8, 2012

[15] D. Bekiaris, G. Economakos, E.S. Xanthopoulos, D. Soudris, "Low-power reconfigurable component utilization in a high-level synthesis flow," International Conference on Reconfigurable computing and FPGAs, pp 428-433, 2011

[16] C. Wolinski, K. Kuchcinski, E. Raffin, F. Charot, "Architecture-driven synthesis of reconfigurable cells," Euromicro conference on digital system design/architecture, methods and tools, pp 531-538, 2009

[17] R. Tessier, "Power-efficient RAM mapping algorithms for FPGA embedded memory blocks," IEEE Transactions on CAD of Integrated Circuits and Systems, vol 26, no.2, 2007

[18] F. Ferrandi, P. L. Lanzi, G. Palermo, C. Pilato, D. Sciuto, A. Tumeo, "An evolutionary approach to area-time optimization of FPGA designs," International Conference on Embedded Systems: Architectures, Modeling and Simulation, IC-SAMOS, pp 145-152, , 2007

[19] J. M. Chang, M. Pedram, "Register allocation and binding for low power," Proceedings of ACM/IEEE Design Automation Conference, DAC '95, pp 29-35, 1995

[20] E. Kursun, R. Mukherjee, S. O. Memik," Early quality assessment for low power behavioral synthesis. Journal of Low Power Electronics, vol 1, no.3, pp 1-13, 2005
[CrossRef]


[21] S. H. Gerez, "Algorithms for VLSI design automation, John Wiley and Sons, 2000

[22] D. Chen, J. Cong, "Register binding and port assignment for multiplexer optimization. In: ASP-DAC '04 Proceedings of the 2004 Asia and South Pacific Design Automation Conference, pp. 68-73, 2004

[23] R. K. Ahuja, T. L. Magnanti, J.B. Orlin, "Network Flows: Theory, Algorithms, and Applications," Prentice-Hall, Englewood Cliffs, NJ, 1993.

[24] Mediabench benchmark suite Available [Online] Available: Temporary on-line reference link removed - see the PDF document

[25] Xpower estimator user guide [Online] Available: Temporary on-line reference link removed - see the PDF document



References Weight

Web of Science® Citations for all references: 8,584 TCR
SCOPUS® Citations for all references: 13,004 TCR

Web of Science® Average Citations per reference: 330 ACR
SCOPUS® Average Citations per reference: 500 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 2016-12-06 00:49 in 50 seconds.




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