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ESUML-EAF: a framework to develop an energy-efficient design model for embedded software

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Abstract

There is a growing interest in developing embedded systems that consume low energy in such application areas as mobile communications or wireless sensor networks. To especially provide the complex and diverse functions of embedded software with limited energy consumption, many studies of low-energy software are being performed. The existing studies to analyze energy consumption of embedded software have mainly focused on source code. However, some studies recently explored model-based energy consumption analysis to fulfill the requirement of energy consumption in the early phase of software development process. This paper proposes a model-based energy consumption analysis framework to develop an energy-efficient design model of embedded software. The proposed framework can analyze energy consumption without building an additional analysis model in software development and provide the chance to fulfill the energy consumption requirements in the early phase of the software development process, which can reduce the feedback efforts.

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References

  1. Prehofer, C.: An adaptive control model for non-functional feature interactions. In: Proceedings of 37th EUROMICRO Conference on Software Engineering and Advanced Applications (SEAA), pp. 501–507 (2011)

  2. Siegmund, N., et al.: SPL conqueror: toward optimization of non-functional properties in software product lines. Softw. Qual. J. 20(3–4), 487–517 (2012)

    Article  Google Scholar 

  3. Saadatmand, M., et al.: A methodology for designing energy-aware secure embedded systems. In: Proceedings of 6th IEEE International Symposium on Industrial Embedded Systems (SIES), pp. 87–90 (2011)

  4. Fei, Y., et al.: Energy-optimizing source code transformations for operating system-driven embedded software. ACM Trans. Embed. Comput. Syst. 7(1), 1–26 (2007)

    Article  Google Scholar 

  5. Tiwari, V., Malik, S., Wolfe, A.: Power analysis of embedded software: a first step towards software power minimization. IEEE Trans. VLSI Syst. 2(4), 437–445 (1994)

    Article  Google Scholar 

  6. Chang, N., Kim, K.H., Lee, H.G.: Cycle-accurate energy consumption measurement and analysis: case study of ARM7TDMI. In: Proceedings of the International Symposium on Low Power Electronics and Design, pp. 185–190 (2000)

  7. Gebotys, C.H., Gebotys, R.J.: An empirical comparison of algorithmic, instruction and architectural power prediction models for high performance embedded DSP processors. In: Proceedings of IEEE International Symposium on Low Power Electronics and Design, pp. 121–123 (1998)

  8. Klass, B., et al.: Modeling inter-instruction energy effects in a digital signal processor. In: Proceeding of the Power Driven Microarchitecture Workshop in ISCA’98 (1998)

  9. Lee, M.T., et al.: Power analysis and minimization techniques for embedded DSP software. IEEE Trans. VLSI Syst. 5(1), 123–135 (1997)

    Article  Google Scholar 

  10. Qu, G., et al.: Code coverage-based power estimation techniques for microprocessors. J. Circuits Syst. Comput. 11(5), 1–18 (2002)

    Google Scholar 

  11. Sarta, D., Trifone, D., Ascia, G.: A data dependent approach to instruction level power estimation. In: Proceedings of IEEE Alessandro Volta Memorial Workshop on Low Power Design, pp. 182–190 (1999)

  12. Talarico, C., et al.: A new framework for power estimation of embedded systems. IEEE Comput. 38, 71–78 (2005)

    Google Scholar 

  13. Tan, T.K., et al.: High-level energy macromodeling of embedded software. IEEE Trans. Comput. Aided Design Integr. Circuits Syst. 21(9), 605–610 (2002)

    Google Scholar 

  14. Tan, T.K., Raghunathan, A., Jha, N.K.: Software architectural transformation: a new approach to low energy embedded software. In: Proceedings of the Design, Automation and Test in Europe Conference and Exhibition, pp. 1046–1051 (2003)

  15. OMG Unified Modeling Language: Superstructure, V2.1.2. http://www.omg.org

  16. Kim, D.H., Kim, J.P., Hong, J.E.: A power consumption analysis technique using UML-based design models in embedded software development. In: Lecture Notes of Computer Science, Vol. 6543, pp. 320–331 (2011)

  17. Tan, T.K., Raghunathan, A., Jha, N.K.L.: EMSIM: An energy simulation framework for an embedded operating system. In: Proceedings of International Symposium of Circuits and Systems, pp. 464–467 (2002)

  18. Sinha, A., Chandrakasan, A.P.: JouleTrack—a web based tool for software energy profiling. In: Proceedings of 38th IEEE Conference on Design Automation, pp. 220–225 (2001)

  19. Brandolese, C., et al.: An instruction-level functionality-based energy estimation model for 32-bits microprocessors. In: Proceedings of ACM/IEEE DAC 2000, pp. 346–351 (2000)

  20. Senn, E.: SoftExplorer: estimating and optimizing the power and energy consumption of a C program for DSP application. EURASIP J. Appl. Signal Process. 16, 2641–2654 (2005)

    Article  Google Scholar 

  21. Yue, X., et al.: OOEM: object-oriented energy model for embedded software reuse. In: Proceedings of IEEE International Conference on Information Reuse and Integration, pp. 551–558 (2003)

  22. Jun, H., et al.: Modeling and analysis of power consumption for component-based embedded software. In: Proceedings of EUC Workshop 2006, pp. 795–804 (2006)

  23. Meedeniya, I., Buhnova, B., Aleti, A., Grunske L.: Architecture-driven reliability and energy optimization for complex embedded systems. In: Proceedings of QoSA 2010, pp. 52–67 (2010)

  24. Selic, B.: The pragmatics of model-driven development. IEEE Softw. 20, 19–25 (2003)

    Article  Google Scholar 

  25. Cook, S.: Looking back at UML. Softw. Syst. Model. 11(4), 471–480 (2012)

    Google Scholar 

  26. Brooks, D., Tiwari, V., Martonosi, M.: Wattch: A framework for architectural-level power analysis and optimizations. In: Proceedings of International Symposium on Computer, Architecture, pp. 83–94 (2000)

  27. Herczeg, Z., Kiss, A., Schmidt, D., Wehn, N.: XEEMU: an improved XScale power simulator. LNCS 4644, 300–309 (2007)

    Google Scholar 

  28. ESUML: ESUML: embedded software modeling using UML 2.x. (2009). http://selab.cbnu.ac.kr/projects/esuml/index.html/

  29. Bultan, T.: Action language: a specification language for model checking reactive systems. In: Proceedings of International Conference on Software Engineering, pp. 335–344 (2000)

  30. Garousi, V., Briand, L.C., Labiche, Y.: Control flow analysis of UML 2.0 sequence diagrams. In: Lecture Notes of Computer Science 3748, pp. 160–174 (2005)

  31. Kim, J.P., Kim, D.H., Hong, J.E.: Estimating power consumption of mobile embedded software based on behavioral model. In: Proceedings of ICCE 2010, pp. 105–106 (2010)

  32. Bammi, J.R., et al.: Software performance estimation strategies in a system-level design tool. In: Proceedings of CODES 2000, pp. 82–86 (2000)

  33. Jeon, S., Hong, J., Song, I., Bae, D.: Developing platform specific model for MPSoC architecture from UML-based embedded software models. J. Syst. Softw. 82, 1695–1708 (2009)

    Article  Google Scholar 

  34. Hennessy, J., Patterson, D.: Computer Architecture: A Quantitative Approach, 3rd edn. Morgan Kaufmann, San Mateo, CA (2003)

    Google Scholar 

  35. DEC: StrongARM RISC Processor, Data Sheet V. 2.0, Digital Equipment Corporation (1996). http://en.wikipedia.org/wiki/StrongARM

  36. NIST: FIPS-PUB-197: Advanced Encryption Standard (2001). http://www.aescrypt.com/aes_information.html

  37. Huang, H., Wu, J.: Novel real-time software-based video coding algorithms. IEEE Trans. Consumer Electron. 39(3), 570–580 (1993)

    Article  Google Scholar 

Download references

Acknowledgments

This research was supported by the Next-generation Information Computing Development Program (2012-0006426) and also partially supported by the Basic Science Research Program (2011-0010396) through the National Research Fund of Korea funded by Ministry of Education, Science, and Technology.

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Correspondence to Jang-Eui Hong.

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Communicated by Dr. Gabor Karsai.

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Kim, DH., Hong, JE. ESUML-EAF: a framework to develop an energy-efficient design model for embedded software. Softw Syst Model 14, 795–812 (2015). https://doi.org/10.1007/s10270-013-0337-5

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