Movatterモバイル変換


[0]ホーム

URL:


Skip to main content

Advertisement

Springer Nature Link
Log in

A gate-level EHW chip: Implementing GA operations and reconfigurable hardware on a single LSI

  • Conference paper
  • First Online:

Abstract

The advantage of Evolvable Hardware (EHW) over traditional hardware is its capacity for dynamic and autonomous adaptation, which is achieved through by Genetic Algorithms (GAs). In most EHW implementations, these GAs are executed by software on a personal computer (PC) or workstation (WS). However, as a wider variety of applications come to utilize EHW, this is not always practical. One solution is to have the GA operations carried out by the hardware itself, by integrating these together with reconfigurable hardware logic like PLA (Programmble Logic Array) or FPGA (Field Programmable Gate Array) on to a single LSI chip. A compact and quickly reconfigurable EHW chip like this could service as an off-the-shelf device for practical applications that require on-line hardware reconfiguration. In this paper, we describe an integrated EHW LSI chip that consists of GA hardware, reconfigurable hardware logic, a chromosome memory, a training data memory, and a 16-bit CPU core (NEC V30). An application of this chip is also described in a myoelectric artificial hand, which is operated by muscular control signals. Although, work on using neural networks for this is being carried out, this approach is not very promising due to the long learning period required for neural networks. A simulation is presented showing that not only is the EHW performance slightly better than with neural networks, but that the learning time is considerably reduced.

This is a preview of subscription content,log in via an institution to check access.

Access this chapter

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. T. Higuchi et al. Evolvable hardware with genetic learning: A first step towards building a darwin machine. InProc. of 2nd International Conference on the Simulation of Adaptive Behavior, pages 417–424. MIP Press, 1993.

    Google Scholar 

  2. D.E. Goldberg.Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, 1989.

    Google Scholar 

  3. Maxime Geoke et al. Online autonomous evolware. InEvolvable Systems: From Biology to Hardware. Springer, 1996.

    Google Scholar 

  4. Masafumi Uchida et al. Control of a robot arm by myoelectric potential. InJournal of Robotics and Mechatronics vol.5 no.3, pages 259–265, 1993.

    Google Scholar 

  5. O. Fukuda et al. An emg controlled robotic manipulator using neural networks. InIEEE ROMAN’97, 1997.

    Google Scholar 

  6. D. Keymeulen et al. Robot learning using gate-level evolvable hardware. InSixth European Workshop on Learning Robots (EWLR-6), 1998.

    Google Scholar 

  7. Stephen D. Scott et al. Hga: A hardware-based genetic algorithm. InProc. of the 1995 ACM/SIGDA Third Int. Symposium on Field-Programmable Gate Arrays, pages 53–59, 1995.

    Google Scholar 

  8. Paul Graham and Brent Nelson. Genetic algorithms in software and in hardware — a performance analysis of warkstation and custom computing machine implementation. InProc. of the IEEE symposium on FPGAs for Custom Computing Machines, pages 216–225, 1996.

    Google Scholar 

  9. Mehrdad Salami. Multiple genetic algorithm processor for hardware optimization. InEvolvable Systems: From Biology to Hardware, pages 249–259, 1996.

    Google Scholar 

  10. Norihiko Yoshida et al. Gap: Generic vlsi processor for genetic algorithms. InSecond Int’l ICSC Symp. on Soft Computing, pages 341–345, 1997.

    Google Scholar 

  11. N. Yoshida et al. Vlsi architecture for steady-state genetic algorithms (in japanese). Technical report, Research Reports on Information Science and Electrical Engineering of Kyushu University Vol.3 No.1, 1998.

    Google Scholar 

  12. Barry Shackleford et al. Hardware framework for accelerating the execution speed of a genetic algorithm. InIEICE Trans. Electron Vol. E80-C NO.7, pages 962–969, 1997.

    Google Scholar 

  13. Dirk Thierens and D.E. Goldberg. Elitist recombination: an integrated selection recombination ga. InProceedings of First IEEE conference on Evolutionary Computation, pages 508–512, 1994.

    Google Scholar 

  14. Dirk Thierens. Selection schemes, elitist recombination and selection intensity. InICGA97, pages 152–159, 1997.

    Google Scholar 

  15. C.H.M. and van Kemenade. Cross-competition between building blocks — propagating information to subsequent generations-. InICGA97, pages 2–9, 1997.

    Google Scholar 

  16. G. Syswerda. Uniform crossover in genetic algorithms. InICGA89, pages 2–9, 1989.

    Google Scholar 

  17. Peter D. Hortensius et al. Parallel random number generation for vlsi systems using cellular automata. InIEEE Trans. on COMPUTERS vol.38 NO. 10, pages 1466–1473, 1989.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Tsukuba, 1-1-1 Tennoudai, Tsukuba, Ibaraki, Japan

    Isamu Kajitani & Tsutomu Hoshino

  2. Hokkaido University, North 13 West 8, Sapporo, Japan

    Daisuke Nishikawa & Hiroshi Yokoi

  3. Adaptive Devices NEC Laboratory, Real World Computing Partnership, Tokyo, Japan

    Shougo Nakaya, Tsukasa Yamauchi, Takeshi Inuo & Nobuki Kajihara

  4. Electrotechinical Laboratory, 1-1-4 Umezono, Tsukuba, Ibaraki, Japan

    Masaya Iwata, Didier Keymeulen & Tetsuya Higuchi

Authors
  1. Isamu Kajitani

    You can also search for this author inPubMed Google Scholar

  2. Tsutomu Hoshino

    You can also search for this author inPubMed Google Scholar

  3. Daisuke Nishikawa

    You can also search for this author inPubMed Google Scholar

  4. Hiroshi Yokoi

    You can also search for this author inPubMed Google Scholar

  5. Shougo Nakaya

    You can also search for this author inPubMed Google Scholar

  6. Tsukasa Yamauchi

    You can also search for this author inPubMed Google Scholar

  7. Takeshi Inuo

    You can also search for this author inPubMed Google Scholar

  8. Nobuki Kajihara

    You can also search for this author inPubMed Google Scholar

  9. Masaya Iwata

    You can also search for this author inPubMed Google Scholar

  10. Didier Keymeulen

    You can also search for this author inPubMed Google Scholar

  11. Tetsuya Higuchi

    You can also search for this author inPubMed Google Scholar

Editor information

Moshe Sipper Daniel Mange Andrés Pérez-Uribe

Rights and permissions

Copyright information

© 1998 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kajitani, I.et al. (1998). A gate-level EHW chip: Implementing GA operations and reconfigurable hardware on a single LSI. In: Sipper, M., Mange, D., Pérez-Uribe, A. (eds) Evolvable Systems: From Biology to Hardware. ICES 1998. Lecture Notes in Computer Science, vol 1478. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0057602

Download citation

Publish with us


[8]ページ先頭

©2009-2025 Movatter.jp