Skip to main content
SpringerLink
Log in

Efficient Evolutionary Approaches for the Data Ordering Problem with Inversion

  • Conference paper
Applications of Evolutionary Computing (EvoWorkshops 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3907))

Included in the following conference series:

Abstract

An important aim of circuit design is the reduction of the power dissipation. Power consumption of digital circuits is closely related to switching activity. Due to the increase in the usage of battery driven devices (e.g. PDAs, laptops), the low power aspect became one of the main issues in circuit design in recent years. In this context, the Data Ordering Problem with and without Inversion is very important. Data words have to be ordered and (eventually) negated in order to minimize the total number of bit transitions. These problems have several applications, like instruction scheduling, compiler optimization, sequencing of test patterns, or cache write-back. This paper describes two evolutionary algorithms for the Data Ordering Problem with Inversion (DOPI). The first one sensibly improves the Greedy Min solution (the best known related polynomial heuristic) by a small amount of time, by successively applying mutation operators. The second one is a hybrid genetic algorithm, where a part of the population is initialized using greedy techniques. Greedy Min and Lower Bound algorithms are used for verifying the performance of the presented Evolutionary Algorithms (EAs) on a large set of experiments. A comparison of our results to previous approaches proves the efficiency of our second approach. It is able to cope with data sets which are much larger than those handled by the best known EAs. This improvement comes from the synchronized strategy of applying the genetic operators (algorithm design) as well as from the compact representation of the data (algorithm implementation).

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Chandrakasan, A.P., Potkonjak, M., Rabaey, J., Brodersen, R.W.: HYPER-LP: a system for power minimization using architectural transformations. In: Int’l Conf on CAD, pp. 300–303 (1992)

    Google Scholar 

  2. Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, 2nd edn. The MIT Press, Cambridge (2001)

    MATH  Google Scholar 

  3. Davis, L.: Applying adaptive algorithms to epistatic domains. In: Proceedings of IJCAI, pp. 162–164 (1985)

    Google Scholar 

  4. Davis, L.: Handbook of Genetic Algorithms. van Nostrand Reinhold, New York (1991)

    Google Scholar 

  5. De Micheli, G.: Synthesis and Optimization of Digital Circuits. McGraw-Hill, Inc., New York (1994)

    Google Scholar 

  6. Devadas, S., Malik, S.: A survey of optimization techniques targeting low power VLSI circuits. In: Design Automation Conf., pp. 242–247 (1995)

    Google Scholar 

  7. Drechsler, N., Drechsler, R.: Exploiting don’t cares during data sequencing using genetic algorithms. In: ASP Design Automation Conf., pp. 303–306 (1999)

    Google Scholar 

  8. Drechsler, R.: Evolutionary Algorithms for VLSI CAD. Kluwer Academic Publishers, Dordrecht (1998)

    Google Scholar 

  9. Drechsler, R., Drechsler, N.: Evolutionary Algorithms for Embedded System Design. Kluwer Academic Publishers, Dordrecht (2002)

    MATH  Google Scholar 

  10. Drechsler, R., Göckel, N.: A genetic algorithm for data sequencing. Electronic Letters 33(10), 843–845 (1997)

    Article  Google Scholar 

  11. Drechsler, R., Drechsler, N.: Minimization of Transitions by Complementation and Resequencing using Evolutionary Algorithms. In: Gedeon, T(T.) D., Fung, L.C.C. (eds.) AI 2003. LNCS (LNAI), vol. 2903. Springer, Heidelberg (2003)

    Google Scholar 

  12. Garey, M.R., Johnson, D.S.: Computers and Intractability – A Guide to NPCompleteness. Freeman, San Francisco (1979)

    Google Scholar 

  13. Goldberg, D.E., Lingle, R.: Alleles, loci, and the traveling salesman problem. In: Int’l Conference on Genetic Algorithms, pp. 154–159 (1985)

    Google Scholar 

  14. Holland, J.H.: Adaption in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor (1975)

    MATH  Google Scholar 

  15. Iman, S., Pedram, M.: Multilevel network optimization for low power. In: Int’l Conf. On CAD, pp. 372–377 (1994)

    Google Scholar 

  16. Mazumder, P., Rudnick, E.: Genetic Algorithms for VLSI Design, Layout & Test Automation. Prentice-Hall, Englewood Cliffs (1998)

    Google Scholar 

  17. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, Heidelberg (1996)

    MATH  Google Scholar 

  18. Murgai, R., Fujita, M., Krishnan, S.C.: Data sequencing for minimum-transition transmission. In: IFIP Int’l Conf. on VLSI (1997)

    Google Scholar 

  19. Murgai, R., Fujita, M., Oliveira, A.: Using complementation and resequencing to minimize transitions. In: Design Automation Conf., pp. 694–697 (1998)

    Google Scholar 

  20. Oliver, I.M., Smith, D.J., Holland, J.R.C.: A study of permutation crossover operators on the traveling salesman problem. In: Int’l Conference on Genetic Algorithms, pp. 224–230 (1987)

    Google Scholar 

  21. Shen, W.-Z., Lin, J.-Y., Wang, F.-W.: Transistor reordering rules for power reduction in CMOS gates. In: ASP Design Automation Conf., pp. 1–6 (1995)

    Google Scholar 

  22. Stan, M., Burleson, W.: Limited-weight codes for low-power I/O. In: Int’l Workshop on Low Power Design (1994)

    Google Scholar 

  23. Tiwari, V., Malik, S., Wolfe, A., Lee, M.: Power analysis of embedded software: A first step towards software power minimization. In: Int’l Conf. on CAD, pp. 384–390 (1994)

    Google Scholar 

  24. Tiwari, V., Malik, S., Wolfe, A., Lee, M.: Instruction level power analysis and optimization software. In: VLSI Design Conf. (1996)

    Google Scholar 

  25. Tsui, C., Pedram, M., Despain, A.M.: Technology decomposition and mapping targeting low power dissipation. In: Design Automation Conf., pp. 68–73 (1993)

    Google Scholar 

  26. Vaishnav, H., Pedram, M.: PCUBE: A performance driven placement algorithm for low power design. In: European Design Automation Conf., pp. 72–77 (1993)

    Google Scholar 

  27. Whitley, D., Starkweather, T., Fuquay, D.: Scheduling problems and traveling salesman: The genetic edge recombination operator. In: Int’l Conference on Genetic Algorithms, pp. 133–140 (1989)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Computer Science, University of Bremen, Bremen, 28359, Germany

    Doina Logofatu & Rolf Drechsler

Authors
  1. Doina Logofatu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rolf Drechsler
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Johannes Gutenberg University, Mainz, Germany

    Franz Rothlauf

  2. Institute AIFB, University of Karlsruhe, 76128, Karlsruhe, Germany

    Jürgen Branke

  3. Dipartimento di Ingegneria dell’Informazione, Università di Parma,  

    Stefano Cagnoni

  4. Centre of Informatics and Systems of the University of Coimbra,  

    Ernesto Costa

  5. Dept. LCC, Universidad de Málaga, Spain

    Carlos Cotta

  6. Institute of Computer Science, University of Bremen, 28359, Bremen, Germany

    Rolf Drechsler

  7. INRIA Saclay - Ile-de-France, Parc Orsay Université, 4, rue Jacques Monod, 91893, ORSAY Cedex, France

    Evelyne Lutton

  8. CISUC, Department of Informatics Engineering, University of Coimbra, Polo II of the University of Coimbra, 3030, Coimbra, Portugal

    Penousal Machado

  9. Dartmouth College, Lebanon, NH, USA

    Jason H. Moore

  10. Universidade de A Coruña, CP 15071, A Coruña, Spain

    Juan Romero

  11. School of Computing Sciences, UEA Norwich, University of East Anglia, NR4 7TJ, Norwich, UK

    George D. Smith

  12. Dipartimento di Automatica e Informatica, Politecnico di Torino, Italy

    Giovanni Squillero

  13. Kyushu University, Japan

    Hideyuki Takagi

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Logofatu, D., Drechsler, R. (2006). Efficient Evolutionary Approaches for the Data Ordering Problem with Inversion. In: Rothlauf, F., et al. Applications of Evolutionary Computing. EvoWorkshops 2006. Lecture Notes in Computer Science, vol 3907. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11732242_29

Download citation

  • DOI: https://doi.org/10.1007/11732242_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33237-4

  • Online ISBN: 978-3-540-33238-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation