Advertisement

Soft Computing

, Volume 18, Issue 8, pp 1515–1527 | Cite as

GRASP with ejection chains for the dynamic memory allocation in embedded systems

  • Marc Sevaux
  • André Rossi
  • María Soto
  • Abraham Duarte
  • Rafael Martí
Methodologies and Application

Abstract

In the design of electronic embedded systems, the allocation of data structures to memory banks is a main challenge faced by designers. Indeed, if this optimization problem is solved correctly, a great improvement in terms of efficiency can be obtained. In this paper, we consider the dynamic memory allocation problem, where data structures have to be assigned to memory banks in different time periods during the execution of the application. We propose a GRASP to obtain high quality solutions in short computational time, as required in this type of problem. Moreover, we also explore the adaptation of the ejection chain methodology, originally proposed in the context of tabu search, for improved outcomes. Our experiments with real and randomly generated instances show the superiority of the proposed methods compared to the state-of-the-art method.

Keywords

Data Structure Local Search Embed System External Memory Local Search Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This research was partially supported by the grant-invited -Professors-UBS-2012 of France, and by the the Ministerio de Economía y Competitividad of Spain (TIN2009-07516 and TIN2012-35632-C02), and the Generalitat Valenciana (Prometeo 2013/049).

References

  1. Chimientia A, Fanucci L, Locatellic R, Saponarac S (2002) VLSI architecture for a low-power video codec system. Microelectron J 33(5):417–427CrossRefGoogle Scholar
  2. Coussy P, Casseau E, Bomel P, Baganne A, Martin E (2006) A formal method for hardware IP design and integration under I/O and timing constraints. ACM Trans Embed Comput Syst 5(1):29–53CrossRefGoogle Scholar
  3. Duarte A, Martí R, Resende MGC, Silva RMA (2011) Grasp with path relinking heuristics for the antibandwidth problem. Networks 58(3):171–189CrossRefzbMATHMathSciNetGoogle Scholar
  4. Feo TA, Resende MGC (1989) A probabilistic heuristic for a computationally difficult set covering problem. Oper Res Lett 8:67–71CrossRefzbMATHMathSciNetGoogle Scholar
  5. Feo TA, Resende MGC (1995) Greedy randomized adaptive search procedures. J Glob Optim 6:109–133CrossRefzbMATHMathSciNetGoogle Scholar
  6. Glover F, Laguna M (1997) Tabu search. Kluwer Academic Publishers, New YorkCrossRefzbMATHGoogle Scholar
  7. Julien N, Laurent J, Senn E, Martin E (2003) Power consumption modeling and characterization of the TI C6201. IEEE Micro 23(5):40–49CrossRefGoogle Scholar
  8. Lin S, Kernighan B (1973) An effective heuristic algorithm for the traveling salesman problem. Operat Res 21:498–516CrossRefzbMATHMathSciNetGoogle Scholar
  9. Lozano M, Duarte A, Gortzar F, Martí R (2012) Variable neighborhood search with ejection chains for the antibandwidth problem. J Heuristics 18:919–938CrossRefGoogle Scholar
  10. Martí R, Duarte A, Laguna M (2009) Advanced scatter search for the max-cut problem. INFORMS J Comput 21(1):26–38CrossRefzbMATHMathSciNetGoogle Scholar
  11. Martí R, Pantrigo JJ, Duarte A, Campos V (2011) Scatter search and path relinking: a tutorial on the linear arrangement problem. Int J Swarm Intell Res 2(2):1–21 Google Scholar
  12. Porumbel D (2009) DIMACS graphs: benchmark instances and best upper bound.Google Scholar
  13. Resende MGC, Martí R, Gallego M, Duarte A (2010) Grasp and path relinking for the max–min diversity problem. Comput Operat Res 37:498–508CrossRefzbMATHGoogle Scholar
  14. Resende MGC, Ribeiro CC (2010) Handbook of Metaheuristics. In: Potvin JY, Gendrau M (eds) Greedy randomized adaptive search procedures, 2nd edn. Kluwer Academic Publishers, New York, pp 283–320Google Scholar
  15. Soto M, Rossi A, Sevaux M (2010) Métaheuristiques pour l’allocation de mémoire dans les systèmes embarqués. In: Proceedings of ROADEF 11e congrès de la société Française de Recherche Opérationelle est d’Aide à la Décision. Toulouse, France, pp 35–43Google Scholar
  16. Soto M, Rossi A, Sevaux M (2011) A mathematical model and a metaheuristic approach for a memory allocation problem. Journal of Heuristics 18(1):149–167CrossRefGoogle Scholar
  17. Soto M, Rossi A, Sevaux M (2011) Two iterative metaheuristic approaches to dynamic memory allocation for embedded systems. In: Merz P, Hao JK (eds) Evolutionary computation in combinatorial optimization—11th European Conference, EvoCOP 2011, Torino, Italy. Proceedings, vol 6622 of Lecture Notes in Computer Science Springer, 250–261Google Scholar
  18. Wuytack S, Catthoor F, Nachtergaele L, De Man H (1996) Power exploration for data dominated video application. In: Proceedings of IEEE International Symposium on Low Power Electronics and Design. Monterey, USA, pp 359–364Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Marc Sevaux
    • 1
  • André Rossi
    • 1
  • María Soto
    • 2
  • Abraham Duarte
    • 3
  • Rafael Martí
    • 4
  1. 1.Université de Bretagne-Sud, Lab-STICC, CNRS Centre de rechercheLorient CedexFrance
  2. 2.Université de Technologie de TroyesTroyesFrance
  3. 3.Dept. Ciencias de la ComputaciónUniversidad Rey Juan CarlosMóstoles, MadridSpain
  4. 4.Dept. de Estadística e Investigación OperativaUniversidad de ValenciaBurjassot (Valencia)Spain

Personalised recommendations