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Multi-objective algorithms for the application mapping problem in heterogeneous multiprocessor embedded system design

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Abstract

Design at the Electronic System-Level tackles the increasing complexity of embedded systems by raising the level of abstraction in system specification and modeling. Two important steps in this process are evaluation of a single design configuration and design space exploration. The exponential size of the design space, along with the complex task of simulating a single design point, makes it impossible to explore the design space efficiently in almost all MPSoC design situations. In order to overcome this problem, one or both of the main steps of the design process (i.e., simulation and exploration) must be accelerated. In this paper, for the first part of the design process, high-level analytical models for application mapping and evaluation are presented in order to accelerate the evaluation of a single design configuration. In the second part of the design process, two multi-objective optimization algorithms that are based on particle swarm optimization and simulated annealing have been proposed for performing design space exploration. Considering multimedia applications as case studies, each of these methods produces a set of near-optimal points. Simulation results show that the proposed methods can lead to near-optimal design configurations with acceptable accuracy in a reasonable time.

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References

  1. Ascia G, Catania V, Di Nuovo AG et al (2007) Eficient design space exploration for application specific systems-on-a-chip. J Syst Archit 53(9):733–750

    Article  Google Scholar 

  2. Bhattacharyya SS et al (2013) Handbook of signal processing systems. Springer, Berlin

    Book  Google Scholar 

  3. Bowman VJ, Thieriez H, Zionts S (1976) On the relationship of the Tchebycheff norm and the efficient frontier of multiple-criteria objectives. In: Multiple Criteria Decision Making, pp 76–85

  4. Branca M, Camerini L, Ferrandi F et al. (2009) Evolutionary algorithms for the mapping of pipelined applications onto heterogeneous embedded systems. In: Proceedings of the Annual Conference on Genetic and Evolutionary Computation, pp 1435–1442

  5. Catania V, Palesi M (2006) A multi-objective genetic approach to mapping problem on network-on-chip. In: Proceedings of the International Conference on JUCS, p 22

  6. Chen G, Li F, Son S et al ( 2008) Application mapping for chip multiprocessors. In: Proceedings of ACM/IEEE Design Automation Conference, pp 620–625

  7. Emmerich MTM, Giannakoglou K, Naujoks B (2006) Single- and multi-objective evolutionary optimization assisted by Gaussian random field metamodels. IEEE Trans Evol Comput 10:421–439

    Article  Google Scholar 

  8. Erbas C (2011) System-level modeling and design space exploration for multiprocessor embedded system-on-chip architectures. Ph.D. thesis, Department of Computer Science, University of Amsterdam, Amsterdam, The Netherlands

  9. Gelatt CD Jr, Vecchi M, Kirkpatrik S (1983) Optimization by simulated annealing. Science 220(4598):671–680

    Article  MathSciNet  MATH  Google Scholar 

  10. Gries AM (2004) Methods for evaluating and covering the design space during early design development. Integr VSLI J 38(2):131–183

    Article  Google Scholar 

  11. Kahn G (1974) The semantics of a simple language for parallel programming. In: Proceedings of the IFIP Congress, p 74

  12. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol 4, no 2, pp 1942–1948

  13. Kienhuis ACJ (1999) Design space exploration of stream-based dataflow architectures: methods and tools. Ph.D. dissertation, Delf University of Technology, Delft, Netherlands, January

  14. Knowles J (2006) A hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems. IEEE Trans Evol Comput 10(1):127–132

    Article  Google Scholar 

  15. Lai MC, Gao L, Wang Z (2010) Exploration and implementation of a highly efficient processor element for multimedia and signal processing domains. IET Comput Digit Tech 4(5):374–387

    Article  Google Scholar 

  16. Lukasiewycz M, Glab M, Haubelt C et al (2008) Efficient symbolic multi-objective design space exploration. In: Proceedings of Asia and South Pacific Design Automation Conference, USA, pp 691–696

  17. Mariani G, Brankovic A, Palermo G et al (2010) A correlation-based design space exploration methodology for multi-processor systems-on-chip. In: Proceedings of 47th Design Automation Conference (DAC), pp 120–125

  18. Metropolis N, Rosenbluth A, Teller A, Teller E (1953) Optimization by simulated annealing. Equation of state calculation by fast computing machines. J Chem Phys 21(1953):1087–1092

    Article  Google Scholar 

  19. Nam D, Park C (2000) Multiobjective simulated annealing: a comparative study to evolutionary algorithms. Int J Fuzzy Syst 2(2):87–97

    Google Scholar 

  20. Orsila H, Salminen E, Hamalainen TD (2009) Parameterizing simulated annealing for distributing Kahn process networks on multiprocessor socs. In: Proceedings of the International Conference on System-on-Chip, pp 19–26

  21. Palermo G, Silvano C, Zaccaria V (2009) ReSPIR: a response surface-based Pareto iterative refinement for application-specific design space exploration. Trans Comput Aided Des Integr Circuit Syst 28(12):1816–1829

    Article  Google Scholar 

  22. Pimentel AD, Thompson M, Polstra S et al (2008) Calibration of abstract performance models for system-level design space exploration. J Signal Process Syst 50(2):99–114

    Article  Google Scholar 

  23. Quan W, Pimentel A (2013) An iterative multi-application mapping algorithm for heterogeneous MPSoCs. In: Embedded Systems for Real-Time Multimedia (ESTIMedia), pp 115–124

  24. Reyes-Siria M, Coello Coello CA (2006) Multi-objective particle swarm optimizers: a survey of the state-of-the-art. Int J Comput Intell 2(3):287–308

    MathSciNet  Google Scholar 

  25. Satish N, Ravindran K, Keutzer K (2007) A decomposition-based constraint optimization approach for statically scheduling task graphs with communication delays to multiprocessors. In: Proceedings of the International Conference on Design, Automation and Test in Europe, pp 57–62

  26. Schafer BC, Wakabayashi K (2012) Machine learning predictive modelling high-level synthesis design space exploration. IET Comput Digit Tech 6(3):153–159

    Article  Google Scholar 

  27. Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: Evolutionary Computation Proceedings, pp 69–73

  28. Sinaei S, Fatemi O (2016) Novel heuristic multi-objective algorithms for mapping and design space exploration of heterogeneous multiprocessor embedded architectures. In: Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP), pp 801–804

  29. Singh AK, Shafique M, Kumar A et al (2013) Mapping on multi/many-core systems: survey of current and emerging trends. In: Proceedings of 50th Annual Design Automation Conference (DAC13), p 1

  30. Steuer RE (1986) Multiple criteria optimization: theory, computation and application. Wiley, New York

    MATH  Google Scholar 

  31. Thiele L, Bacivarov I, Haid W et al (2010) Mapping applications to tiled multiprocessor embedded systems. In: Proceedings of the International Conference on Application of Concurrency to System Design, pp 29–40

  32. Zitzler E, Thiele L (1998) Multiobjective optimization using evolutionary algorithms—a comparative case study. In: Proceedings of the International Conference on Parallel Problem Solving from Nature

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  1. Faculty of Electrical and Computer Engineering, University of Tehran, Tehran, Iran

    Sima Sinaei & Omid Fatemi

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  1. Sima Sinaei
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  2. Omid Fatemi
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Correspondence to Omid Fatemi.

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Sinaei, S., Fatemi, O. Multi-objective algorithms for the application mapping problem in heterogeneous multiprocessor embedded system design. J Supercomput 75, 4150–4176 (2019). https://doi.org/10.1007/s11227-018-2442-2

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  • DOI: https://doi.org/10.1007/s11227-018-2442-2

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