Advertisement

Optimization Strategies in Design Space Exploration

  • Jacopo PaneratiEmail author
  • Donatella Sciuto
  • Giovanni Beltrame
Reference work entry

Abstract

This chapter presents guidelines to choose an appropriate exploration algorithm, based on the properties of the design space under consideration. The chapter describes and compares a selection of well-established multi-objective exploration algorithms for high-level design that appeared in recent scientific literature. These include heuristic, evolutionary, and statistical methods. The algorithms are divided into four sub-classes and compared by means of several metrics: their setup effort, convergence rate, scalability, and performance of the optimization. The common goal of these algorithms is the optimization of a multi-processor platform running a set of diverse software benchmark applications. Results show how the metrics can be related to the properties of a target design space (size, number of variables, and variable ranges) with a focus on accuracy, precision, and performance.

Acronyms

ADRS

Average Distance from Reference Set

ANN

Artificial Neural Network

DoE

Design of Experiments

DSE

Design Space Exploration

EA

Evolutionary Algorithm

GA

Genetic Algorithm

ILP

Integer Linear Program

MDP

Markov Decision Process

MPSoC

Multi-Processor System-on-Chip

NN

Neural Network

PSO

Particle Swarm Optimization

RSM

Response Surface Modeling

SA

Simulated Annealing

SoC

System-on-Chip

References

  1. 1.
    Beltrame G, Nicolescu G (2011, in press) A multi-objective decision-theoretic exploration algorithm for platform-based design. In: Proceedings of design, automation and test in Europe (DATE)Google Scholar
  2. 2.
    Beltrame G, Fossati L, Sciuto D (2009) ReSP: a nonintrusive transaction-level reflective MPSoC simulation platform for design space exploration. IEEE Trans Comput Aided Des Integr Circuits Syst 28(12):1857–1869CrossRefGoogle Scholar
  3. 3.
    Beltrame G, Fossati L, Sciuto D (2010) Decision-theoretic design space exploration of multiprocessor platforms. IEEE Trans Comput Aided Des Integr Circuits Syst 29(7):1083–1095. doi:  10.1109/TCAD.2010.2049053 CrossRefGoogle Scholar
  4. 4.
    Coello CA (2000) An updated survey of ga-based multiobjective optimization techniques. ACM Comput Surv 32(2):109–143. doi:  10.1145/358923.358929 CrossRefGoogle Scholar
  5. 5.
    Czyzżak P, Jaszkiewicz A (1998) Pareto simulated annealing–a metaheuristic technique for multiple-objective combinatorial optimization. J Multi-Criteria Decis Anal 7(1):34–47. doi: 10.1002/(SICI)1099-1360(199801)7:1<34::AID-MCDA161>3.0.CO;2-6Google Scholar
  6. 6.
    Deb K, Goel T (2001) Controlled elitist non-dominated sorting genetic algorithms for better convergence. In: Zitzler E, Thiele L, Deb K, Coello Coello C, Corne D (eds) Evolutionary multi-criterion optimization. Lecture notes in Computer Science, vol 1993. Springer, Heidelberg, pp 67–81. doi:  10.1007/3-540-44719-9_5 CrossRefGoogle Scholar
  7. 7.
    Durillo JJ, Nebro AJ (2011) jMetal: a java framework for multi-objective optimization. Adv Eng Softw 42:760–771CrossRefGoogle Scholar
  8. 8.
    Erbas C (2006) System-level modelling and design space exploration for multiprocessor embedded system-on-chip architectures. Amsterdam University Press, AmsterdamCrossRefGoogle Scholar
  9. 9.
    Fonseca CM, Fleming PJ (1995) An overview of evolutionary algorithms in multiobjective optimization. Evol Comput 3(1):1–16. doi:  10.1162/evco.1995.3.1.1 CrossRefGoogle Scholar
  10. 10.
    Fornaciari W, Sciuto D, Silvano C, Zaccaria V (2002) A sensitivity-based design space exploration methodology for embedded systems. Des Autom Embed Syst 7(1):7–33. doi:  10.1023/A:1019791213967 CrossRefzbMATHGoogle Scholar
  11. 11.
    Givargis T, Vahid F, Henkel J (2001) System-level exploration for pareto-optimal configurations in parameterized systems-on-a-chip. In: 2001 IEEE/ACM international conference on computer aided design, ICCAD 2001, pp 25–30. doi:  10.1109/ICCAD.2001.968593 Google Scholar
  12. 12.
    Ishibuchi H, Murata T (1996) Multi-objective genetic local search algorithm. In: Proceedings of IEEE international conference on evolutionary computation, pp 119–124. doi:  10.1109/ICEC.1996.542345 Google Scholar
  13. 13.
    Ishibuchi H, Murata T (1998) A multi-objective genetic local search algorithm and its application to flowshop scheduling. IEEE Trans Syst Man Cybern C Appl Rev 28(3):392–403. doi:  10.1109/5326.704576 CrossRefGoogle Scholar
  14. 14.
    Jaddoe S, Pimentel AD (2008) Signature-based calibration of analytical system-level performance models. In: Proceedings of the 8th international workshop on embedded computer systems: architectures, modeling, and simulation SAMOS’08. Springer, Heidelberg, pp 268–278CrossRefGoogle Scholar
  15. 15.
    Jaszkiewicz A (2004) A comparative study of multiple-objective metaheuristics on the bi-objective set covering problem and the pareto memetic algorithm. Ann Oper Res 131:135–158. doi:  10.1023/B:ANOR.0000039516.50069.5b MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Jaszkiewicz A, Dabrowski G (2005) MOMH: multiple objective meta heuristics. Available at the web site http://home.gna.org/momh/
  17. 17.
    Jia ZJ, Bautista T, Núñez A, Pimentel AD, Thompson M (2013) A system-level infrastructure for multidimensional MP-SoC design space co-exploration. ACM Trans Embed Comput Syst 13(1s):27:1–27:26. doi:  10.1145/2536747.2536749
  18. 18.
    Kaelbling LP, Littman ML, Moore AP (1996) Reinforcement learning: a survey. J Artif Intell Res 4:237–285Google Scholar
  19. 19.
    Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings IEEE international conference on neural networks, vol 4, pp 1942–1948. doi:  10.1109/ICNN.1995.488968
  20. 20.
    Lukasiewycz M, Glay M, Haubelt C, Teich J (2008) Efficient symbolic multi-objective design space exploration. In: ASP-DAC ’08: proceedings of the 2008 Asia and South Pacific design automation conference. IEEE Computer Society Press, Seoul, pp 691–696Google Scholar
  21. 21.
    Mariani G, Brankovic A, Palermo G, Jovic J, Zaccaria V, Silvano C (2010) A correlation-based design space exploration methodology for multi-processor systems-on-chip. In: 2010 47th ACM/IEEE design automation conference (DAC), pp 120–125Google Scholar
  22. 22.
    Mohanty S, Prasanna VK, Neema S, Davis J (2002) Rapid design space exploration of heterogeneous embedded systems using symbolic search and multi-granular simulation. SIGPLAN Not 37(7):18–27CrossRefGoogle Scholar
  23. 23.
    Nebro A, Durillo J, Coello C (2013) Analysis of leader selection strategies in a multi-objective particle swarm optimizer. In: 2013 IEEE congress on evolutionary computation (CEC), pp 3153–3160. doi:  10.1109/CEC.2013.6557955
  24. 24.
    Nebro AJ, Durillo JJ, Machín M, Coello Coello CA, Dorronsoro B (2013) A study of the combination of variation operators in the NSGA-II algorithm. In: Advances in artificial intelligence: 15th conference of the Spanish association for artificial intelligence, CAEPIA 2013, Madrid, 17–20 Sept 2013. Proceedings. Springer, Heidelberg, pp 269–278. doi:  10.1007/978-3-642-40643-0_28 CrossRefGoogle Scholar
  25. 25.
    Okabe T, Jin Y, Sendhoff B (2003) A critical survey of performance indices for multi-objective optimisation. In: The 2003 congress on, evolutionary computation, CEC ’03, vol 2, pp 878–885. doi:  10.1109/CEC.2003.1299759
  26. 26.
    Palermo G, Silvano C, Zaccaria V (2008) Discrete particle swarm optimization for multi-objective design space exploration. In: 11th EUROMICRO conference on digital system design architectures, methods and tools, DSD’08, pp 641–644. doi:  10.1109/DSD.2008.21
  27. 27.
    Palermo G, Silvano C, Zaccaria V (2009) ReSPIR: a response surface-based pareto iterative refinement for application-specific design space exploration. IEEE Trans Comput Aided Des Integr Circuits Syst 28(12):1816–1829. doi:  10.1109/TCAD.2009.2028681 CrossRefGoogle Scholar
  28. 28.
    Palesi M, Givargis T (2002) Multi-objective design space exploration using genetic algorithms. In: CODES ’02: Proceedings of the tenth international symposium on hardware/software codesign. ACM, Colorado, pp 67–72. doi:  10.1145/774789.774804 Google Scholar
  29. 29.
    Panerati J, Beltrame G (2014) A comparative evaluation of multi-objective exploration algorithms for high-level design. ACM Trans Des Autom Electron Syst 19(2):15:1–15:22. doi:  10.1145/2566669
  30. 30.
    Russell SJ, Norvig P (1995) Artificial intelligence: a modern approach, 1st edn. Prentice Hall, Upper Saddle RiverzbMATHGoogle Scholar
  31. 31.
    Serafini P (1994) Simulated annealing for multi objective optimization problems. In: Tzeng G, Wang H, Wen U, Yu P (eds) Multiple criteria decision making. Springer, New York, pp 283–292. doi:  10.1007/978-1-4612-2666-6_29 CrossRefGoogle Scholar
  32. 32.
    Sheldon D, Vahid F, Lonardi S (2007) Soft-core processor customization using the design of experiments paradigm. In: DATE conference, pp 1–6Google Scholar
  33. 33.
    Sivanandam SN, Deepa SN (2007) Introduction to genetic algorithms, 1st edn. Springer, Berlin/New YorkzbMATHGoogle Scholar
  34. 34.
    Srinivas N, Deb K (1994) Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evol Comput 2(3):221–248. doi:  10.1162/evco.1994.2.3.221 CrossRefGoogle Scholar
  35. 35.
    Taghavi T, Pimentel AD (2011) Design metrics and visualization techniques for analyzing the performance of moeas in DSE. In: ICSAMOS, pp 67–76Google Scholar
  36. 36.
    Ulungu E, Teghem J, Fortemps P, Tuyttens D (1999) MOSA method: a tool for solving multiobjective combinatorial optimization problems. J Multi-Criteria Decis Anal 8(4):221–236. doi: 10.1002/(SICI)1099-1360(199907)8:4<221::AID-MCDA247>3.0.CO;2-OGoogle Scholar
  37. 37.
    Zaccaria V, Palermo G, Castro F, Silvano C, Mariani G (2010) Multicube explorer: an open source framework for design space exploration of chip multi-processors. In: 2PARMA: proceedings of the workshop on parallel programming and run-time management techniques for many-core architectures, HannoverGoogle Scholar
  38. 38.
    Zitzler E, Thiele L (1999) Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Trans Evol Comput 3(4):257–271. doi:  10.1109/4235.797969 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  • Jacopo Panerati
    • 1
    Email author
  • Donatella Sciuto
    • 2
  • Giovanni Beltrame
    • 1
  1. 1.Polytechnique MontréalMontrealCanada
  2. 2.Politecnico di MilanoMilanoItaly

Personalised recommendations