During development, processor architectures can be tuned and configured by many different parameters. For benchmarking, automatic design space explorations (DSEs) with heuristic algorithms are a helpful approach to find the best settings for these parameters according to multiple objectives, e.g. performance, energy consumption, or real-time constraints. But if the setup is slightly changed and a new DSE has to be performed, it will start from scratch, resulting in very long evaluation times.

To reduce the evaluation times we extend the NSGA-II algorithm in this article, such that automatic DSEs can be supported with a set of transformation rules defined in a highly readable format, the fuzzy control language (FCL). Rules can be specified by an engineer, thereby representing existing knowledge. Beyond this, a decision tree classifying high-quality configurations can be constructed automatically and translated into transformation rules. These can also be seen as very valuable result of a DSE because they allow drawing conclusions on the influence of parameters and describe regions of the design space with high density of good configurations.   

Our evaluations show that automatically generated decision trees can classify near optimal configurations for the hardware parameters of the Grid ALU Processor (GAP) and M-Sim 2. Further evaluations show that automatically constructed transformation rules can reduce the number of evaluations required to reach the same quality of results as without rules by 43%, leading to a significant saving of time of about 25%. In the demonstrated example using rules also leads to better results.


Design Space Fuzzy Rule Pareto Front Mutation Operator Transformation Rule 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ascia, G., Catania, V., Nuovo, A.G.D., Palesi, M., Patti, D.: Efficient design space exploration for application specific systems-on-a-chip. Journal of Systems Architecture 53(10), 733–750 (2007); Embedded Computer Systems: Architectures, Modeling, and SimulationCrossRefGoogle Scholar
  2. 2.
    Beltrame, G., Bruschi, D., Sciuto, D., Silvano, C.: Decision-theoretic exploration of multiprocessor platforms. In: Proceedings of the 4th International Conference on Hardware/Software Codesign and System Synthesis, CODES+ISSS 2006, pp. 205–210 (October 2006)Google Scholar
  3. 3.
    Calborean, H.: Multi-Objective Optimization of Advanced Computer Architectures using Domain-Knowledge. PhD thesis, Lucian Blaga University of Sibiu, Romania (PhD Supervisor: Prof. Lucian Vintan, PhD) (2011)Google Scholar
  4. 4.
    Calborean, H., Jahr, R., Ungerer, T., Vintan, L.: Optimizing a superscalar system using multi-objective design space exploration. In: Proceedings of the 18th International Conference on Control Systems and Computer Science (CSCS), Bucharest, Romania, Calea Grivitei, nr. 132, 78122, Sector 1, Bucuresti, May 24-27, vol. 1, pp. 339–346. Editura Politehnica Press (2011) ISSN 2066-4451Google Scholar
  5. 5.
    Calborean, H., Vintan, L.: An automatic design space exploration framework for multicore architecture optimizations. In: 9th Roedunet International Conference (RoEduNet), Sibiu, Romania, pp. 202–207 (June 2010)Google Scholar
  6. 6.
    Calborean, H., Vintan, L.: Toward an efficient automatic design space exploration frame for multicore optimization. In: ACACES 2010 Poster Abstracts, Terassa, Spain, pp. 135–138 (July 2010)Google Scholar
  7. 7.
    Cook, H., Skadron, K.: Predictive design space exploration using genetically programmed response surfaces. In: Proceedings of the 45th annual Design Automation Conference, DAC 2008, pp. 960–965. ACM, New York (2008)CrossRefGoogle Scholar
  8. 8.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)CrossRefGoogle Scholar
  9. 9.
    Guthaus, M., Ringenberg, J., Ernst, D., Austin, T., Mudge, T., Brown, T.: Mibench: A free, commercially representative embedded benchmark suite. In: 4th IEEE International Workshop on Workload Characteristics, pp. 3–14 (December 2001)Google Scholar
  10. 10.
    Hall, M.: Correlation-based Feature Selection for Machine Learning. PhD thesis, University of Waikato (1999)Google Scholar
  11. 11.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. Newsl. 11, 10–18 (2009)CrossRefGoogle Scholar
  12. 12.
    IEC 1131 - programmable controllers, part 7 - fuzzy control programming (January 1997)Google Scholar
  13. 13.
    Jahr, R., Ungerer, T., Calborean, H., Vintan, L.: Automatic multi-objective optimization of parameters for hardware and code optimizations. In: Smari, W.W., McIntire, J.P. (eds.) Proceedings of the 2011 International Conference on High Performance Computing & Simulation (HPCS 2011), pp. 308–316. IEEE (July 2011) ISBN 978-1-61284-381-0Google Scholar
  14. 14.
    Joseph, K.G., Sharkey, J., Ponomarev, D.: M-sim: A flexible, multithreaded architectural simulation environment. Technical Report CS-TR-05-DP01, State University of New York at Binghamton (October 2005)Google Scholar
  15. 15.
    Mariani, G., Brankovic, A., Palermo, G., Jovic, J., Zaccaria, V., Silvano, C.: A correlation-based design space exploration methodology for multi-processor systems-on-chip. In: Proceedings of the 47th Design Automation Conference, DAC 2010, pp. 120–125. ACM, New York (2010)Google Scholar
  16. 16.
    Mariani, G., Palermo, G., Silvano, C., Zaccaria, V.: Meta-model assisted optimization for design space exploration of multi-processor systems-on-chip. In: Proceedings of the 2009 12th Euromicro Conference on Digital System Design, Architectures, Methods and Tools, DSD 2009, pp. 383–389. IEEE Computer Society, Washington, DC (2009)CrossRefGoogle Scholar
  17. 17.
    Mariani, G., Palermo, G., Silvano, C., Zaccaria, V.: Multi-processor system-on-chip design space exploration based on multi-level modeling techniques. In: International Symposium on Systems, Architectures, Modeling, and Simulation, SAMOS 2009, pp. 118 –124 (July 2009)Google Scholar
  18. 18.
    Mariani, G., Palermo, G., Zaccaria, V., Silvano, C.: An efficient design space exploration methodology for multi-cluster vliw architectures based on artificial neural networks. In: Proc. IFIP International Conference on Very Large Scale Integration VLSI - SoC 2008, Rhodes Island, Greece, October 13-15 (2008)Google Scholar
  19. 19.
    Nebro, A., Durillo, J., Garcıa-Nieto, J., Coello, C.A., Luna, F., Alba, E.: SMPSO: A new pso-based metaheuristic for multi-objective optimization. In: Proceedings of the IEEE Symposium Series on Computational Intelligence, pp. 66–73 (2009)Google Scholar
  20. 20.
    Ozisikyilmaz, B., Memik, G., Choudhary, A.: Efficient system design space exploration using machine learning techniques. In: Proceedings of the 45th Annual Design Automation Conference, DAC 2008, pp. 966–969. ACM, New York (2008)CrossRefGoogle Scholar
  21. 21.
    Palermo, G., Silvano, C., Zaccaria, V.: Discrete particle swarm optimization for multi-objective design space exploration. In: Proceedings of the 2008 11th EUROMICRO Conference on Digital System Design Architectures, Methods and Tools, pp. 641–644. IEEE Computer Society, Washington, DC (2008)CrossRefGoogle Scholar
  22. 22.
    Quinlan, J.R.: C4.5: Programs for Machine Learning, 1st edn. Morgan Kaufmann Series in Machine Learning. Morgan Kaufmann (January 1993)Google Scholar
  23. 23.
    Roychowdhury, S., Pedrycz, W.: A survey of defuzzification strategies. International Journal of Intelligent Systems 16(6), 679–695 (2001)CrossRefzbMATHGoogle Scholar
  24. 24.
    Sengupta, A., Sedaghat, R., Zeng, Z.: Rapid design space exploration by hybrid fuzzy search approach for optimal architecture determination of multi objective computing systems. Microelectronics Reliability 51, 502–512 (2010); 2010 Reliability of Compound Semiconductors (ROCS) Workshop; Prognostics and Health ManagementCrossRefGoogle Scholar
  25. 25.
    Shehan, B., Jahr, R., Uhrig, S., Ungerer, T.: Reconfigurable grid ALU processor: Optimization and design space exploration. In: Proceedings of the 13th Euromicro Conference on Digital System Design (DSD), Lille, France (2010)Google Scholar
  26. 26.
    Sierra, M.R., Coello Coello, C.A.: Improving PSO-Based Multi-objective Optimization Using Crowding, Mutation and ε-Dominance. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 505–519. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  27. 27.
    Uhrig, S., Shehan, B., Jahr, R., Ungerer, T.: The two-dimensional superscalar gap processor architecture. International Journal on Advances in Systems and Measurements 3(1 and 2), 71–81 (2010)Google Scholar
  28. 28.
    Waterman, T.: Adaptive compilation and inlining. PhD thesis. Houston, TX, USA, Adviser-Cooper Keith D. (2006)Google Scholar
  29. 29.
    Zeng, Z., Sedaghat, R., Sengupta, A.: A framework for fast design space exploration using fuzzy search for vlsi computing architectures. In: Proceedings of 2010 IEEE International Symposium on Circuits and Systems (ISCAS), May 30-June 2, pp. 3176–3179 (2010)Google Scholar
  30. 30.
    Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength pareto evolutionary algorithm. Technical Report 103, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH), Zurich, Switzerland (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ralf Jahr
    • 1
  • Horia Calborean
    • 2
  • Lucian Vintan
    • 2
  • Theo Ungerer
    • 1
  1. 1.Institute of Computer ScienceUniversity of AugsburgAugsburgGermany
  2. 2.Computer Science & Engineering Department“Lucian Blaga” University of SibiuSibiuRomania

Personalised recommendations