skip to main content
research-article

Parameter Space Representation of Pareto Front to Explore Hardware-Software Dependencies

Published:09 September 2015Publication History
Skip Abstract Section

Abstract

Embedded systems design requires conflicting objectives to be optimized with an appropriate choice of hardware-software parameters. A simulation campaign can guide the design in finding the best trade-offs, but due to the big number of possible configurations, it is often unfeasible to simulate them all. For these reasons, design space exploration algorithms aim at finding near-optimal system configurations by simulating only a subset of them.

In this work, we present PS, a new multiobjective optimization algorithm, and evaluate it in the context of the embedded system design. The basic idea is to recognize interesting regions—that is, regions of the configuration space that provide better configurations with respect to other ones. PS evaluates more configurations in the interesting regions while less thoroughly exploring the rest of the configuration space. After a detailed formal description of the algorithm and the underlying concepts, we show a case study involving the hardware/software exploration of a VLIW architecture. Qualitative and quantitative comparisons of PS against a well-known multiobjective genetic approach demonstrate that while not outperforming it in terms of Pareto dominance, the proposed approach can balance the uniformity and granularity qualities of the solutions found, obtaining more extended Pareto fronts that provide a wider view of the potentiality of the designed device. Therefore, PS represents a further valid choice for the designer when objective constrains allow it.

References

  1. Santosh G. Abraham, B. Ramakrishna Rau, and Robert Schreiber. 2000. Fast Design Space Exploration Through Validity and Quality Filtering of Subsystem Designs. Technical Report HPL-2000-98. HP Laboratories, Palo Alto, CA.Google ScholarGoogle Scholar
  2. Giuseppe Ascia, Vincenzo Catania, Alessandro G. Di Nuovo, Maurizio Palesi, and Davide Patti. 2011. Performance evaluation of efficient multi-objective evolutionary algorithms for design space exploration of embedded computer systems. Applied Soft Computing 11, 1, 382--398. DOI:http://dx.doi.org/10.1016/j.asoc.2009.11.029 Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Giuseppe Ascia, Vincenzo Catania, and Maurizio Palesi. 2002. Tuning methodologies for parameterized systems design. In Proceedings of the International Workshop on System-on-Chip for Real-Time Applications. 71--82.Google ScholarGoogle Scholar
  4. Giuseppe Ascia, Vincenzo Catania, Maurizio Palesi, and Davide Patti. 2005. A system-level framework for evaluating area/performance/power trade-offs of VLIW-based embedded systems. In Proceedings of the Asia and South Pacific Design Automation Conference (ASP-DAC’05). 940--943. http://www.scopus.com/inward/record.url?eid=2-s2.0-49749111784&partnerID=40&md5=59dcbd7d5d390c3d69c693f3cdbac7cb. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Pradnya S. Borkar and Anjali R. Mahajan. 2014. Types and applications of parallel genetic algorithm. International Journal of Advanced Research in Computer Science and Software Engineering 4, 4, 1387--1390.Google ScholarGoogle Scholar
  6. Erick Cantú-Paz. 1998. A survey of parallel genetic algorithms. Calculateurs Paralleles 10, 2, 141--171.Google ScholarGoogle Scholar
  7. Vincenzo Catania, Andrea Araldo, and Davide Patti. 2014. Complete results of Paramspace tests. Retrieved August 14, 2015, from https://sites.google.com/site/xedivad/home/research/PS_TEST.tgz?attredirects=0&d=1.Google ScholarGoogle Scholar
  8. Vincenzo Catania, Maurizio Palesi, and Davide Patti. 2008. Reducing complexity of multiobjective design space exploration in VLIW-based embedded systems. ACM Transactions on Architecture and Code Optimization 5, 2, Article No. 11. DOI:http://dx.doi.org/10.1145/1400112.1400116 Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Carlos A. Coello Coello, David A. Van Veldhuizen, and Gary B. Lamont. 2002. Evolutionary Algorithms for Solving Multi-Objective Problems, Vol. 5. Kluwer Academic.Google ScholarGoogle Scholar
  10. Matej Črepinšek, Shih-Hsi Liu, and Marjan Mernik. 2013. Exploration and exploitation in evolutionary algorithms: A survey. ACM Computing Surveys 45, 3, 35. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Michael Dellnitz, Oliver Schütze, and Thorsten Hestermeyer. 2005. Covering Pareto sets by multilevel subdivision techniques. Journal of Optimization Theory and Applications 124, 1, 113--136.Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Michael Dellnitz and Katrin Witting. 2009. Computation of robust Pareto points. International Journal of Computing Science and Mathematics 2, 3, 243--266. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Agoston E. Eiben and Cornelis A. Schippers. 1998. On evolutionary exploration and exploitation. Fundamenta Informaticae 35, 1, 35--50. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. William Fornaciari, Donatella Sciuto, Cristina Silvano, and Vittorio Zaccaria. 2001. A design framework to efficiently explore energy-delay tradeoffs. In Proceedings of the 9th International Symposium on Hardware/Software Co-Design. 260--265. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Tony Givargis, Frank Vahid, and Jörg Henkel. 2002. System-level exploration for Pareto-optimal configurations in parameterized system-on-a-chip. IEEE Transactions on Very Large Scale Integration (VLSI) Systems 10, 2, 416--422. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Daniel Kahneman. 1973. Attention and Effort. Prentice Hall.Google ScholarGoogle Scholar
  17. Vinod Kathail, Michael S. Schlansker, and B. Ramakrishna Rau. 2000. HPL-PD Architecture Specification: Version 1.0. Technical Report HPL-93-80. Compiler and Architecture Research HP Laboratories, Palo Alto, CA.Google ScholarGoogle Scholar
  18. Joshua Knowles, Lothar Thiele, and Eckart Zitzler. 2006. A Tutorial on the Performance Assessment of Stochastive Multiobjective Optimizers. Technical Report 214. Computer Engineering and Networks Laboratory, ETH Zurich, Switzerland.Google ScholarGoogle Scholar
  19. Vilfredo Pareto. 1896. Cours D’Economie Politique. Vols. I and II. F. Rouge, Lausanne, Switzerland.Google ScholarGoogle Scholar
  20. Davide Patti, Maurizio Palesi, and Vincenzo Catania. 2014. Merging compilation and microarchitectural configuration spaces for performance/power optimization in VLIW-based systems. In Modern Trends and Techniques in Computer Science. Advances in Intelligent Systems and Computing, Vol. 285. Springer, 203--212. DOI:http://dx.doi.org/10.1007/978-3-319-06740-7_18Google ScholarGoogle Scholar
  21. George Sperling and Erich Weichselgartner. 1995. Episodic theory of the dynamics of spatial attention. Psychological Review 102, 3, 503--532.Google ScholarGoogle ScholarCross RefCross Ref
  22. Trimaran. 2010. An Infrastructure for Research in Instruction-Level Parallelism. Available at http://www.trimaran.org/.Google ScholarGoogle Scholar
  23. Thomas Weise, Raymond Chiong, and Ke Tang. 2012. Evolutionary optimization: Pitfalls and booby traps. Journal of Computer Science and Technology 27, 5, 907--936.Google ScholarGoogle ScholarCross RefCross Ref
  24. WSTS. 2013. World Semiconductor Trade Statistics Bluebook. Available at http://www.wsts.org/.Google ScholarGoogle Scholar
  25. Eckart Zitzler, Kalyanmoy Deb, and Lothar Thiele. 2000. Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation 8, 2, 173--195. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Eckart Zitzler, Marco Laumanns, and Lothar Thiele. 2001. SPEA2: Improving the performance of the strength Pareto evolutionary algorithm. In Proceedings of EUROGEN 2001: Evolutionary Methods for Design, Optimization, and Control with Applications to Industrial Problems. 95--100.Google ScholarGoogle Scholar
  27. Eckart Zitzler, Lothar Thiele, Marco Laumanns, Carlos M. Fonseca, and Viviane Grunert da Fonseca. 2003. Performance assessment of multiobjective optimizers: An analysis and review. IEEE Transactions on Evolutionary Computation 7, 2, 117--132. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Parameter Space Representation of Pareto Front to Explore Hardware-Software Dependencies

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in

      Full Access

      • Article Metrics

        • Downloads (Last 12 months)2
        • Downloads (Last 6 weeks)0

        Other Metrics

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader
      About Cookies On This Site

      We use cookies to ensure that we give you the best experience on our website.

      Learn more

      Got it!