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.
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Chimientia A, Fanucci L, Locatellic R, Saponarac S (2002) VLSI architecture for a low-power video codec system. Microelectron J 33(5):417–427
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):29–53
Duarte A, Martí R, Resende MGC, Silva RMA (2011) Grasp with path relinking heuristics for the antibandwidth problem. Networks 58(3):171–189
Feo TA, Resende MGC (1989) A probabilistic heuristic for a computationally difficult set covering problem. Oper Res Lett 8:67–71
Feo TA, Resende MGC (1995) Greedy randomized adaptive search procedures. J Glob Optim 6:109–133
Glover F, Laguna M (1997) Tabu search. Kluwer Academic Publishers, New York
Julien N, Laurent J, Senn E, Martin E (2003) Power consumption modeling and characterization of the TI C6201. IEEE Micro 23(5):40–49
Lin S, Kernighan B (1973) An effective heuristic algorithm for the traveling salesman problem. Operat Res 21:498–516
Lozano M, Duarte A, Gortzar F, Martí R (2012) Variable neighborhood search with ejection chains for the antibandwidth problem. J Heuristics 18:919–938
Martí R, Duarte A, Laguna M (2009) Advanced scatter search for the max-cut problem. INFORMS J Comput 21(1):26–38
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
Porumbel D (2009) DIMACS graphs: benchmark instances and best upper bound.
Resende MGC, Martí R, Gallego M, Duarte A (2010) Grasp and path relinking for the max–min diversity problem. Comput Operat Res 37:498–508
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 283–320
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 35–43
Soto M, Rossi A, Sevaux M (2011) A mathematical model and a metaheuristic approach for a memory allocation problem. Journal of Heuristics 18(1):149–167
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, 250–261
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 359–364
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).
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Communicated by E. Viedma.
Appendix: Best known solutions
Appendix: Best known solutions
Table 7 shows in the first column the name of the instance, in the second column the best known value, which appears in bold when our new methods are able to improve it in this experiment w.r.t the best previously identified. The next column presents the solution value reached by the ILP formulation in Soto et al. (2011) solved with Xpress-MP, that is used as a heuristic when the time limit of 1 h is reached: the best solution found so far is then returned by the solver. Note that in some large instances, this method is not able to provide a solution within the 3,600 s of time limit considered. The following three columns show the deviation value with respect to the best known value for the IM, CPA, and GRASP methods, and the associated CPU time in seconds which is the same for the three algorithms. Finally, the last two columns show the Dev. BK value and the CPU time for the GRASP+EC method, respectively.
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Sevaux, M., Rossi, A., Soto, M. et al. GRASP with ejection chains for the dynamic memory allocation in embedded systems. Soft Comput 18, 1515–1527 (2014). https://doi.org/10.1007/s00500-013-1157-9
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DOI: https://doi.org/10.1007/s00500-013-1157-9