Skip to main content
SpringerLink
Log in

Hardware implementation of a neural network for high energy physics application

  • Conference paper
  • First Online:
New Trends in Neural Computation (IWANN 1993)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 686))

Included in the following conference series:

  • 300 Accesses

Abstract

The high speed and parallelism of VLSI Analog Neural Networks make them specially attractive for the treatment of data coming from elementary particle accelerators, which are used in high energy physics. In this paper we show the implementation of an analog neural network with low precision weights, devoted to the reconstitution of tracks: capability of handling 600 pixels/chip at about 2 1012 connections/second, in 40 mm2 (1.5 um ES2) at 100 Mhz.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. B. Denby. “Pattern Recognition for High Energy Physics with Neural Networks”. Proc. of Neural Networks: from Biology to High Energy Physics. pp. 353–381. 1991.

    Google Scholar 

  2. G. Stimpf-Abele, L.I. Grarrido, V. Gaitan. “Track Finding with Neural Networks vs. Standard Methods”. First Itnl. Elba Workshop on Neural Networks: From Biology to High Energy Physics. 1991.

    Google Scholar 

  3. L.O. Chua, L. Yang. “Cellular Neural Networks: Theory”. IEEE Trans on Circuits and Systems. Vol. 35, pp. 1257–1272, 1988.

    Google Scholar 

  4. L.O. Chua, L. Yang. “Cellular Neural Networks: Applications”. Id, pp. 1273–1290,1988.

    Google Scholar 

  5. J. Carrabina. “High Speed/capacity VLSI Neural Networks”. PhD. Dissertation. October 1991. Universitat Autònoma de Barcelona.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Centre National de Microelectrònica, Universitat Autònoma de Barcelona, 08193, Bellaterra, Barcelona

    J. Carrabina, F. Lisa, V. Gaitan, L. Garrido & E. Valderrama

Authors
  1. J. Carrabina
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. F. Lisa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. V. Gaitan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. L. Garrido
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. E. Valderrama
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

José Mira Joan Cabestany Alberto Prieto

Rights and permissions

Reprints and permissions

Copyright information

© 1993 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Carrabina, J., Lisa, F., Gaitan, V., Garrido, L., Valderrama, E. (1993). Hardware implementation of a neural network for high energy physics application. In: Mira, J., Cabestany, J., Prieto, A. (eds) New Trends in Neural Computation. IWANN 1993. Lecture Notes in Computer Science, vol 686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-56798-4_183

Download citation

  • DOI: https://doi.org/10.1007/3-540-56798-4_183

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-56798-1

  • Online ISBN: 978-3-540-47741-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation