Skip to main content
Springer Nature Link
Log in

Intelligent Online Configuration for DVFS Multiprocessor Architecture: Fuzzy Approach

  • Conference paper
  • First Online:
Advances and Trends in Artificial Intelligence. From Theory to Practice (IEA/AIE 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11606))

Abstract

The use of fuzzy logic to generate optimal actions for hardware architecture reconfiguration offers flexible and efficient solutions. In this paper, a new fuzzy approach is proposed in order to guarantee the balance between real time periodic application schedulability and energy consumption optimization under multi-core architecture. Dynamic voltage/frequency scaling (DVFS) has been a key technique in exploiting the processors configurable characteristics. However, for large class of applications in embedded real time systems, the variable operating frequency interferes with tasks deadline respect. The problem is seen as multi-criteria multi-objective decision making issue with dependent criteria. The approach calculates, in offline mode and in online mode, the optimal number of activated homogenous cores and their frequency. Simulated and tested on periodic task sets generated with different system charges, the proposed intelligent technique is support decision system that shows significant results.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Pomerol, J.: Artificial intelligence and human decision making. Eur. J. Oper. Res. 2(99), 3–25 (1997)

    Article  Google Scholar 

  2. Nazmul, H., Alam Hossain, Md., Fayezul, I., Priyanka, B., Tahira, Y.: Research on energy efficiency in cloud computing. Int. J. Sci. Eng. Res. 7(8), 358–367 (2016)

    Google Scholar 

  3. Char, J.C., Fakhfakh, A., Couterrier, R., Glerch, A.: Dynamic frequency scaling for energy consumption reduction in synchronous distributed applications. In: 13th IEEE International Symposium on Parallel and Distributed Processing with Applications. IEEE (2016)

    Google Scholar 

  4. Qadri, M.Y., Qadri, N.N., McDonald-Maier, K.D.: Fuzzy logic based energy and throughput aware design space exploration for MPSoCs. Microprocess. Microsyst. 3(2), 68–73 (2015)

    Google Scholar 

  5. Qadri, M.Y., McDonald-Maier, K.D., Qadri, N.N.: Energy and throughputs aware fuzzy logic based reconfiguration for MPSoC. J. Intell. Fuzzy Syst. 3(2), 68–73 (2014)

    Google Scholar 

  6. Fakhfakh, M.M.: Energy consumption optimization of parallel applications with iterations using CPU frequency scaling. Thesis (2016)

    Google Scholar 

  7. Rauber, T., Runger, G., Schwind, M., Xu, M., Melzner, S.: Energy measurement, modeling, and prediction for processors with frequency scaling. J. Supercomput. 70(3), 1451–1476 (2014)

    Article  Google Scholar 

  8. Rountree, B., Lowenthal, D., Funk, S., Freeh, V.W., De Supinski, B., Schulz, M.: Bounding energy consumption in large-scale MPI programs. In: Proceedings of the 2007 ACM/IEEE Conference on, pp. 1–9 (2007)

    Google Scholar 

  9. Cochran, R., Hankendi, C., Coskun, A., Reda, S.,: Identifying the optimal energy-efficient operating points of parallel workloads. In: Proceedings of the International Conference on Computer-Aided Design, ICCAD 2011, IEEE Press, NJ, pp. 608–615 (2011)

    Google Scholar 

  10. Henkel, J., Parameswaran, S.: Designing Embedded Processors: A lower Power Perspective. Springer, Heidelberg (2007). https://doi.org/10.1007/978-1-4020-5869-1

    Book  Google Scholar 

  11. Da Rosa, T.D., Larrea, V., Calazans, N., Gehm-Moraes, F.: Power consumption reduction in MPSoCs through DFS. In: SBCCI, pp. 1–6 (2012)

    Google Scholar 

  12. Parain, F., Banâtre, M., Cabillic, G., Higuera-Toledano, T., Issarny, V.: Lesot: Techniques de réduction de la consommation dans un système embarqué temps réel. Technique et Science Informatiques 20(10), 1247–1278 (2001)

    Google Scholar 

  13. Navet, N., Grajar, B.: Systemés temps réel, Hermes (2006)

    Google Scholar 

  14. Baker, T.P., Cirinei, M.: A necessary and sometimes sufficient condition for the feasibility of sets of sporadic hard-deadline tasks. In: Proceedings of IEEE Real-Time Systems Symposium (RTSS), pp 178–190. IEEE Press (2006)

    Google Scholar 

  15. Jing, L., Luo, Z., Ferry, D., Agrawal, K., Gill, C., Lu, C.: Global EDF scheduling for parallel real time tasks. Real-Time Syst. 51(4), 395–439 (2015)

    Article  Google Scholar 

  16. Ibrahim, A.: Fuzzy Logic for Embedded Systems Applications. Butterworth, Heinemann, Newton (2003)

    Google Scholar 

  17. Najar, Y., Ben Ahmed, S.: Fuzzy multiprocessor architecture reconfiguration based on dynamic frequency scaling. In: Proceedings of ISKE, pp. 761–767. IEEE Press (2017)

    Google Scholar 

  18. Bellman, R., Zadeh, L.A.: Decision making in a fuzzy environment. Manag. Sci. 17, 141–164 (1970)

    Article  MathSciNet  Google Scholar 

  19. Kickert, W.: Fuzzy Theories on Decision Making: A Critical Review. Frontiers in System Research. Springer, Heidelberg (1979)

    MATH  Google Scholar 

  20. IEC, International Standard: Programmable Controllers – Part 7: Fuzzy Control Programming, International Electrotechnical Commission, Geneva, Switzerland, iEC Standard (2000)

    Google Scholar 

  21. Intel. Embedded Ultra-Low Power Intel486 GX Processor. Datasheet, Intel

    Google Scholar 

  22. Chetto, M.: Ordonnancement dans les systèmes temps réel: optimisation de la consommation énergétique. ISTE éditions (2014)

    Google Scholar 

  23. Reza P.H., Echeverri, E.J., Pineda, G.: Synthesis and VHDL implementation of fuzzy logic controller for dynamic voltage and frequency scaling (DVFS) goals in digital processors. In: Fuzzy Logic - Controls, Concepts, Theories and Applications (2012)

    Google Scholar 

  24. Shen, H., Lu, H., Qiu, Q.: Learning based DVFS for simultaneous temperature, performance and energy management. In: ISQED (2012)

    Google Scholar 

  25. Gaurav, D., Tajana, S.R.: Dynamic voltage frequency scaling for multi-tasking systems using online learning. In: ISLPED 2007, Portland, Oregon, USA, pp. 207–212 (2007)

    Google Scholar 

  26. Shaheryar, N., Jameel, A.: Real-time implementation of fuzzy logic based DVFS for Leon3 architecture. Asian J. Eng. Sci. Technol. 8(1) (2018)

    Google Scholar 

  27. Ahmed, J., Siyal, M.Y., Najam, S., Najam, Z.: Fuzzy Logic Based Power-Efficient Real-Time Multi-Core System. SAST. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-3120-5

    Book  Google Scholar 

  28. Carlsson, C., Fullér, R.: Fuzzy multiple criteria decision making: recent developments. Fuzzy Sets Syst. 78, 139–153 (2001)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. High Institute of Computing (ISI), University of Tunis el Manar, Tunis, Tunisia

    Najar Yousra

  2. Faculty of Science, Mathematics, Physics and Natural (FST), University of Tunis el Manar, Tunis, Tunisia

    Ben Ahmed Samir

Authors
  1. Najar Yousra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ben Ahmed Samir
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Najar Yousra .

Editor information

Editors and Affiliations

  1. Institute for Software Technology, Graz University of Technology, Graz, Austria

    Franz Wotawa

  2. Department of Applied Informatics, University of Klagenfurt, Klagenfurt, Austria

    Gerhard Friedrich

  3. Institute for Software Technology, Graz University of Technology, Graz, Austria

    Ingo Pill

  4. Institute for Software Technology, Graz University of Technology, Graz, Austria

    Roxane Koitz-Hristov

  5. Department of Computer Science, Texas State University, San Marcos, TX, USA

    Moonis Ali

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Yousra, N., Samir, B.A. (2019). Intelligent Online Configuration for DVFS Multiprocessor Architecture: Fuzzy Approach. In: Wotawa, F., Friedrich, G., Pill, I., Koitz-Hristov, R., Ali, M. (eds) Advances and Trends in Artificial Intelligence. From Theory to Practice. IEA/AIE 2019. Lecture Notes in Computer Science(), vol 11606. Springer, Cham. https://doi.org/10.1007/978-3-030-22999-3_53

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-22999-3_53

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-22998-6

  • Online ISBN: 978-3-030-22999-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics