Possibilities and Limitations of Applying Evolvable Hardware to Real-World Applications

  • Jim Torresen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1896)

Abstract

Evolvable Hardware (EHW) has been proposed as a new method for designing systems for real-world applications. This paper contains a classi.cation of the published work on this topic. Further, a thorough discussion about the limitations of the present EHW and possible solutions to these are proposed. EHW has been applied to a wide range of applications. However, to solve more complex applications, the evolutionary schemes should be improved.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    T. Higuchi et al. Evolvable hardware: A first step towards building a Darwin machine. In Proc. of the 2nd Int. Conf. on Simulated Behaviour, pages 417–424. MIT Press, 1993.Google Scholar
  2. 2.
    D. Goldberg. Genetic Algorithms in search, optimization, and machine learning. Addison Wesley, 1989.Google Scholar
  3. 3.
    M. Murakawa et al. The grd chip: Genetic recon.guration of dsps for neural network processing. IEEE Transactions on Computers, 48(6):628–638, June 1999.Google Scholar
  4. 4.
    J.D. Lohn and S.P. Colombano. A circuit representation technique for automated circuit design. IEEE Trans. on EvolutionaryComputation, 3(3):205–219, September 1999.Google Scholar
  5. 5.
    J. R. Koza et al. Genetic Programming III. San Francisco, CA: Morgan Kaufmann Publishers, 1999.MATHCrossRefGoogle Scholar
  6. 6.
    J. Torresen. Increased complexity evolution applied to evolvable hardware. In Dagli et al., editors, Smart Engineering System Design: Neural Networks, Fuzzy Logic, Evolutionary Programming, Data Mining, and Complex Systems, Proc. of ANNIE’99. ASME Press, November 1999.Google Scholar
  7. 7.
    E. Takahashi et al. An evolvable-hardware-based clock timing architecture towards gigahz digital systems. In Proc. of the Genetic and EvolutionaryComputation Conference, 1999.Google Scholar
  8. 8.
    J. F. Miller. Digital alter design at gate-level using evolutionary algorithms. In Proc. of the Genetic and EvolutionaryComputation Conference, 1999.Google Scholar
  9. 9.
    M. Murakawa et al. Analogue EHW chip for intermediate frequency filters. In M. Sipper et al., editors, Evolvable Systems: From Biology to Hardware. Second Int. Conf., ICES 98, pages 134–143. Springer-Verlag, 1998. Lecture Notes in Computer Science, vol. 1478.CrossRefGoogle Scholar
  10. 10.
    Sakanashi et al. Evolvable hardware chip for high precision printer image compression. In Proc. of 15th National Conference on Arti.cial Intelligence (AAAI-98), 1998.Google Scholar
  11. 11.
    R. Porter et al. An applications approach to evolvable hardware. In Proc. of the First NASA/DoD Workshop on Evolvable Hardware, 1999.Google Scholar
  12. 12.
    M. Iwata et al. A pattern recognition system using evolvable hardware. In Proc. of Parallel Problem Solving from Nature IV (PPSN IV). Springer Verlag, LNCS 1141, September 1996.CrossRefGoogle Scholar
  13. 13.
    I. Kajitani and other. An evolvable hardware chip and its application as a multifunction prosthetic hand controller. In Proc. of 16th National Conference on Artifcial Intelligence (AAAI-99), 1999.Google Scholar
  14. 14.
    J. Torresen. Scalable evolvable hardware applied to road image recognition. In Proc. of the 2nd NASA/DoD Workshop on Evolvable Hardware. Silicon Valley, USA, July 2000.Google Scholar
  15. 15.
    D. Keymeulen et al. On-line model-based learning using evolvable hardware for a robotics tracking systems. In Genetic Programming 1998: Proc. of the Third Annual Conference, pages 816–823. Morgan Kaufmann, 1998.Google Scholar
  16. 16.
    A. Thompson. Exploration in design space: Unconventional electronics design through artifcial evolution. IEEE Trans. on Evolutionary Computation, 3(3):171–177, September 1999.Google Scholar
  17. 17.
    M. Yasunaga et al. Evolvable sonar spectrum discrimination chip designed by genetic algorithm. In Proc. of 1999 IEEE Systems, Man, and Cybernetics Conference (SMC’99), 1999.Google Scholar
  18. 18.
    I. Kajitani et al. An evolvable hardware chip for prosthetic hand controller. In Proc. of MicroNeuro’99, pages 179–186, 1999.Google Scholar
  19. 19.
    J. Torresen. Evolvable hardware —The coming hardware design method? In N. Kasabov and R. Kozma, editors, Neuro-fuzzyte chniques for Intelligent Information Systems, pages 435–449. Physica-Verlag (Springer-Verlag), 1999.Google Scholar
  20. 20.
    O. Aaserud and I.R. Nielsen. Trends in current analog design: A panel debate. Analog Integrated Circuits and Signal Processing, 7(1):-, 1995.Google Scholar
  21. 21.
    S. J. Flockton and K. Sheehan. Intrinsic circuit evolution using programmable analogue arrays. In M. Sipper et al., editors, Evolvable Systems: From Biology to Hardware. Second Int. Conf., ICES 98, pages 144–153. Springer-Verlag, 1998. Lecture Notes in Computer Science, vol. 1478.CrossRefGoogle Scholar
  22. 22.
    R. S. Zebulum. Analog circuits evolution in extrinsic and intrinsic modes. In M. Sipper et al., editors, Evolvable Systems: From Biology to Hardware. Second Int. Conf., ICES 98, pages 154–165. Springer-Verlag, 1998. Lecture Notes in Computer Science, vol. 1478.CrossRefGoogle Scholar
  23. 23.
    J. Torresen. A divide-and-conquer approach to evolvable hardware. In M. Sipper et al., editors, Evolvable Systems: From Biology to Hardware. Second Int. Conf., ICES 98, pages 57–65. Springer-Verlag, 1998. Lecture Notes in Computer Science, vol. 1478.CrossRefGoogle Scholar
  24. 24.
    J. F. Miller and P. Thomson. Aspects of digital evolution: Geometry and learning. In M. Sipper et al., editors, Evolvable Systems: From Biology to Hardware. Second Int. Conf., ICES 98, pages 25–35. Springer-Verlag, 1998. Lecture Notes in Computer Science, vol. 1478.CrossRefGoogle Scholar
  25. 25.
    T. Kalganova et al. Some aspects of an evolvable hardware approach for multiplevalued combinational circuit design. In M. Sipper et al., editors, Evolvable Systems: From Biologyto Hardware. Second Int. Conf., ICES 98, pages 78–89. Springer-Verlag, 1998. Lecture Notes in Computer Science, vol. 1478.CrossRefGoogle Scholar
  26. 26.
    W-P. Lee et al. Learning complex robot behaviours by evolutionary computing with task decomposition. In Andreas Brink and John Demiris, editors, Learning Robots: Proc. of 6th European Workshop, EWLR-6 Brighton. Springer, 1997.Google Scholar
  27. 27.
    X. Yao and T. Higuchi. Promises and challenges of evolvable hardware. In T. Higuchi et al., editors, Evolvable Systems: From Biology to Hardware. First Int. Conf., ICES 96. Springer-Verlag, 1997. Lecture Notes in Computer Science, vol. 1259.Google Scholar
  28. 28.
    E. Cantu-Paz. A survey of parallel genetic algorithms. Calculateurs Parallels, 10(2), 1998. Paris: Hermes.Google Scholar
  29. 29.
    M. Murakawa et al. Hardware evolution at function level. In Proc. of Parallel Problem Solving from Nature IV (PPSNIV). Springer Verlag, LNCS 1141, September 1996.CrossRefGoogle Scholar
  30. 30.
    J.R. Koza. Future work and practical applications of genetic programming. In Handbook of EvolutionaryComputation, page H1.1:3. IOP Publishing Ltd and Oxford University Press, 1997.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Jim Torresen
    • 1
  1. 1.Department of InformaticsUniversity of OsloBlindernNorway

Personalised recommendations