System Level Hardware–Software Design Exploration with XCS

  • Fabrizio Ferrandi
  • Pier Luca Lanzi
  • Donatella Sciuto
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3103)


The current trend in Embedded Systems (ES) design is moving towards the integration of increasingly complex applications on a single chip. An Embedded System has to satisfy both performance constraints and cost limits; it is composed of both dedicated elements, i.e. hardware (HW) components, and programmable units, i.e. software (SW) components, Hardware (HW) and software (SW) components have to interact with each other for accomplishing a specific task. One of the aims of codesign is to support the exploration of the most significant architectural alternatives in terms of decomposition between hardware (HW) and software (SW) components. In this paper, we propose a novel approach to support the exploration of feasible hardware-software (HW-SW) configurations. The approach exploits the learning classifier system XCS both to identify existing relationships among the system components and to support HW-SW partitioning decisions. We validate the approach by applying it to the design of a Digital Sound Spatializer.


Execution Time Embed System Very Large Scale Integration Learning Classifier System Data Flow Graph 
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  1. 1.
    Ascia, G., Catania, V., Palesi, M.: Parameterized system design based on genetic algorithms. In: Proceedings of the Ninth International Workshop on Hardware/ Software Codesign (April 2001)Google Scholar
  2. 2.
    Eles, P., Kuchcinski, K., Peng, Z., Doboli, A.: System level hardware/software partitioning based on simulated annealing and tabu search. Journal on Design Automation for Embedded Systems 2, 5–32 (1997)CrossRefGoogle Scholar
  3. 3.
    Ferrandi, F., Lanzi, P.L., Sciuto, D.: Mining Interesting Patterns from Hardware-Software Codesign Data with the Learning Classifier System XCS. In: Proceedings of the 2003 Congress on Evolutionary Computation (CEC 2003), Canberra, Australia, December 9-12, pp. 1486–1492. IEEE, Los Alamitos (2003)CrossRefGoogle Scholar
  4. 4.
    Gupta, R.K., De Micheli, G.: Hardware-software cosynthesis for digital systems. Design & Test of Computers, IEEE 10, 29–41 (1993)CrossRefGoogle Scholar
  5. 5.
    Henkel, J., Ernst, R.: An approach to automated hardware/software partitioning using a flexible granularity that is driven by high-level estimation techniques. IEEE Transactions on Very Large Scale Integration (VLSI) Systems 9, 273–289 (2001)CrossRefGoogle Scholar
  6. 6.
    De Micheli, G.: Synthesis and Optimization of Digital Circuits. McGraw-Hill, New York (1994)Google Scholar
  7. 7.
    Palesi, M., Givargis, T.: Multi-objective design space exploration using genetic algorithms. In: Proceedings of the Tenth International Workshop on Hardware/Software Codesign, May 2002, pp. 67–72 (2002)Google Scholar
  8. 8.
    Quan, G., Hu, X., Greenwood, G.: Preference-driven hierarchical hardware/ software partitioning. In: International Conference on Computer Design (ICCD 1999), pp. 652–657 (1999)Google Scholar
  9. 9.
    Sciuto, D., Ferrandi, F., Lanzi, P.L., Tanelli, M.: Systemlevel metrics for hardware/software architectural mapping. In: Proceedings of the 2nd IEEE International Workshop on Electronics Design, Test and Applications (DELTA 2004), Burswood Resort, Perth, Australia (January 2004)Google Scholar
  10. 10.
    Vahid, F., Gajski, D.D.: Closeness metrics for system-level functional partitioning. In: Proceedings EURO-DAC 1995 Design Automation Conference with EUROVHDL, September 1995, pp. 328–333 (1995)Google Scholar
  11. 11.
    Wilson, S.W.: Classifier Fitness Based on Accuracy. Evolutionary Computation 3(2), 149–175 (1995), CrossRefGoogle Scholar
  12. 12.
    Wilson, S.W.: Function approximation with a classifier system. In: Spector, L., Goodman, E.D., Wu, A., Langdon, W.B., Voigt, H.-M., Gen, M., Sen, S., Dorigo, M., Pezeshk, S., Garzon, M.H., Burke, E. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), San Francisco, California, USA, July 7-11, pp. 974–981. Morgan Kaufmann, San Francisco (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Fabrizio Ferrandi
    • 1
  • Pier Luca Lanzi
    • 1
  • Donatella Sciuto
    • 1
  1. 1.Dipartimento di Elettronica e InformazionePolitecnico di MilanoMilanoItaly

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