Abstract
A method of synthesis of software for low-power real-time embedded systems is presented in this paper. A function of the system is specified in the form of the task graph, then it is implemented using embedded processors with low-power and high-performance cores. The power consumption is minimized using the developmental genetic programming. The optimization is based on finding the makespan, satisfying all real-time constraints, for which the power consumption is as low as possible. We present experimental results, obtained for real-life examples and for some standard benchmarks. The results show that our method gives better solutions than makespans obtained using existing methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ditzel, M., Serdijn, W., Otten, R.: Power-Aware Architecting: for Data-Dominated Applications. Springer, Netherlands (2007). http://dx.doi.org/10.1007/978-1-4020-6420-3
Greenhalgh, P.: Big. little processing with ARM CortexTM-A15 & ARM CortexTM-A7, ARM White paper, pp. 1–8 (2011). http://www.arm.com/files/downloads/big.LITTLE_Final.pdf
Benini, L., Bogliolo, A., De Micheli, G.: A survey of design techniques for system-level dynamic power management. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 8(3), 299–316 (2000). http://dx.doi.org/10.1109/92.845896
Shang, L., Dick, R.P., Jha, N.K.: Slopes: hardware– software cosynthesis of low-power real-time distributed embedded systems with dynamically reconfigurable fpgas. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 26(3), 508–526 (2007). http://dx.doi.org/10.1109/TCAD.2006.883909
Steger, C., Bachmann, C., Genser, A., Weiss, R., Haid, J.: Power-aware hardware/software codesign of mobile devices. e & i Elektrotech. Informationstechnik 127(11), 327–334 (2010)
Luo, J., Jha, N.K.: Low power distributed embedded systems: dynamic voltage scaling and synthesis. In: Sahni, S., Prasanna, V.K., Shukla, U. (eds.) HiPC 2002. LNCS, vol. 2552, pp. 679–693. Springer, Heidelberg (2002). doi:10.1007/3-540-36265-7_63
Yao, F., Demers, A., Shenker, S.: A scheduling model for reduced cpu energy. In: Proceedings of the 36th Annual Symposium on Foundations of Computer Science, pp. 374–382. IEEE (1995). http://dx.doi.org/10.1016/j.ejor.2009.11.005
Hartmann, S., Briskorn, D.: A survey of variants and extensions of the resource-constrained project scheduling problem. Eur. J. Oper. Res. EJOR 207(1), 1–15 (2010). http://dx.doi.org/10.1016/j.ejor.2009.11.005
Hartmann, S.: A competitive genetic algorithm for resource-constrained project scheduling. Naval Res. Logistics (NRL) 45(7), 733–750 (1998). http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.45.359&rep=rep1&type=pdf
Li, X., Kang, L., Tan, W.: Optimized research of resource constrained project scheduling problem based on genetic algorithms. In: Kang, L., Liu, Y., Zeng, S. (eds.) ISICA 2007. LNCS, vol. 4683, pp. 177–186. Springer, Heidelberg (2007). doi:10.1007/978-3-540-74581-5_19
Zoulfaghari, H., Nematian, J., Mahmoudi, N., Khodabandeh, M.: A new genetic algorithm for the rcpsp in large scale. Int. J. Appl. Evol. Comput. (IJAEC) 4(2), 29–40 (2013). http://dx.doi.org/10.4018/jaec.2013040103
Jeff, B.: Ten things to know about big.little, ARM Holdings (2013). http://community.arm.com/groups/processors/blog/2013/06/18/ten-things-to-know-about-biglittle
Deniziak, S., Ciopiński, L.: Synthesis of power aware adaptive embedded software using developmental genetic programming. In: Fidanova, S. (ed.) Recent Advances in Computational Optimization. SCI, vol. 655, pp. 97–121. Springer, Heidelberg (2016). doi:10.1007/978-3-319-40132-4_7
Michalewicz, Z.: Genetic Algorithms+Data Structures = Evolution Programs. Springer, Heidelberg (1996). http://dx.doi.org/10.1007/978-3-662-03315-9
Dick, R.P., Jha, N.K.: MOGAC: a multiobjective genetic algorithm for hardware-software cosynthesis of distributed embedded systems. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 17(10), 920–935 (1998). http://dx.doi.org/10.1109/43.728914
Koza, J., Poli, R.: Genetic programming. In: Burke, E., Kendall, G. (eds.) Search Methodologies, pp. 127–164. Springer, US (2005). http://dx.doi.org/10.1007/0-387-28356-0_5
Koza, J.R., Bennett III, F.H., Andre, D., Keane, M.A.: Evolutionary design of analog electrical circuits using genetic programming. In: Parmee, I.C. (ed.) Adaptive Computing in Design and Manufacture, pp. 177–192. Springer, London (1998). http://dx.doi.org/10.1007/978-1-4471-1589-2_14
Deniziak, S., Gorski, A.: Hardware/Software co-synthesis of distributed embedded systems using genetic programming. In: Hornby, G.S., Sekanina, L., Haddow, P.C. (eds.) ICES 2008. LNCS, vol. 5216, pp. 83–93. Springer, Heidelberg (2008). doi:10.1007/978-3-540-85857-7_8
Deniziak, S., Ciopinski, L., Pawinski, G., Wieczorek, K., Bak, S.: Cost optimization of real-time cloud applications using developmental genetic programming. In: IEEE/ACM 7th International Conference on Utility and Cloud Computing, vol. 7269, pp. 182–189. IEEE Computer Society (2014). http://dx.doi.org/10.1109/UCC.2014.126
Briand, L., Labiche, Y.: A uml-based approach to system testing. Softw. Syst. Model. 1(1), 10–42 (2002). http://dx.doi.org/10.1007/s10270-002-0004-8
Dick, R., Rhodes, D., Wolf, W.: TGFF: task graphs for free. In: Proceedings of the Sixth International Workshop on Hardware/Software Codesign (CODES/CASHE 1998), pp. 97–101, March 1998 . http://dx.doi.org/10.1109/HSC.1998.666245
Sapiecha, K., Ciopinski, L., Deniziak, S.: An application of developmental genetic programming for automatic creation of supervisors of multi-task real-time object-oriented systems. In: IEEE Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 501–509 (2014). http://dx.doi.org/10.15439/2014F208
Sapiecha, K., Ciopiński, L., Deniziak, S.: Synthesis of self-adaptive supervisors of multi-task real-time object-oriented systems using developmental genetic programming. In: Fidanova, S. (ed.) Recent Advances in Computational Optimization. SCI, vol. 610, pp. 55–74. Springer, Heidelberg (2016). doi:10.1007/978-3-319-21133-6_4
Pawiński, G., Sapiecha, K.: An efficient solution of the resource constrained project scheduling problem based on an adaptation of the developmental genetic programming. In: Fidanova, S. (ed.) Recent Advances in Computational Optimization. SCI, vol. 610, pp. 205–223. Springer, Heidelberg (2016). doi:10.1007/978-3-319-21133-6_12
Hu, J., Marculescu, R.: Energy-and performance-aware mapping for regular noc architectures. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 24(4), 551–562 (2005). http://dx.doi.org/10.1109/TCAD.2005.844106
E3s benchmark. http://ziyang.eecs.umich.edu/dickrp/e3s/
Han, S., Park, M.: Predictability of least laxity first scheduling algorithm on multiprocessor real-time systems. In: Zhou, X., Sokolsky, O., Yan, L., Jung, E.-S., Shao, Z., Mu, Y., Lee, D.C., Kim, D.Y., Jeong, Y.-S., Xu, C.-Z. (eds.) EUC 2006 Workshops. LNCS, vol. 4097, pp. 755–764. Springer, Heidelberg (2006). doi:10.1007/11807964_76
Sitek, P., Wikarek, J.: A hybrid framework for the modelling and optimisation of decision problems in sustainable supply chain management. Int. J. Prod. Res. 53, 1–18 (2015). http://dx.doi.org/10.1080/00207543.2015.1005762
Sitek, P., Wikarek, J.: A hybrid programming framework for modeling and solving constraint satisfaction and optimization problems. Sci. Program. 2016, 13 (2016). Article ID 5102616. http://dx.doi.org/10.1155/2016/5102616
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Deniziak, S., Ciopinski, L., Pawinski, G. (2017). Synthesis of Low-Power Embedded Software Using Developmental Genetic Programming. In: Janech, J., Kostolny, J., Gratkowski, T. (eds) Proceedings of the 2015 Federated Conference on Software Development and Object Technologies. SDOT 2015. Advances in Intelligent Systems and Computing, vol 511. Springer, Cham. https://doi.org/10.1007/978-3-319-46535-7_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-46535-7_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46534-0
Online ISBN: 978-3-319-46535-7
eBook Packages: EngineeringEngineering (R0)