Nothing Special   »   [go: up one dir, main page]

Skip to main content

A modular VLSI architecture for neural networks implementation

  • Implementation
  • Conference paper
  • First Online:
From Natural to Artificial Neural Computation (IWANN 1995)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 930))

Included in the following conference series:

  • 802 Accesses

Abstract

This paper describes a modular analog VLSI architecture for the implementation of artificial neural networks. Analog neural network implementations are faster and smaller than their digital counterparts, but the problem of smaller dynamic range of the analog weight memory and the linearity of the synapses based on analog multipliers increases the need for design effort at the circuit level. We suggest that a complex neural network system can be implemented in a single chip if a modular architecture design using simple analog circuits is followed. To demonstrate the VLSI implementability of the neural network system, a description of each analog circuit block is provided.

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

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

Bibliography

  1. P.E. Allen and D. R. Holberg, CMOS Analog Circuit Design, New-York, NY, Holt, Rinehart and Winston, Inc., 1987.

    Google Scholar 

  2. L.N.M. Edward, “Comment on “Voltage-Controlled Linear Resistor by Two MOS Transistors and its Application to Active RC Filter MOS Integration”, in Proc. IEEE, Vol. 74, No. 5, pp. 753–755, 1986.

    Google Scholar 

  3. R.L. Geiger, P. E. Allen, and N. R. Strader, VLSI Design Techniques for Analog and Digital Circuits, New-York, NY, McGraw-Hill, 1990.

    Google Scholar 

  4. P.R. Gray and R. G. Meyer, Analysis and Design of Analog Integrated Circuits, 3rd ed., New-York, NY, John Wiley & Sons, 1993.

    Google Scholar 

  5. K.K. Moon, F.J. Kub and I.A. Mack, “Random Address 32×32 Programmable Analog Vector-Matrix Multiplier for Artificial Neural Networks”, in Proc. IEEE CICC '90, pp. 26.7.1–26.7.4, 1990.

    Google Scholar 

  6. V. Radeka, “Fast Analogue Multipliers With Field-Effect Transistors”, IEEE Trans. Nuclear Science, Vol. 11, No. 1, pp. 302–307, 1964.

    Google Scholar 

  7. F.M.A. Salam and M. R. Choi, “Analog MOS Vector Multipliers for the Implementation of Synapses in Artificial Neural Networks”, Journal of Circuits, Systems, and Computers, Vol. 1, No. 2, pp. 205–228, 1991.

    Google Scholar 

  8. S. Satyanarayana, Y. P. Tsividis, and H. P. Graf, “A Reconfigurable VLSI Neural Network”, IEEE J. Solid-State Circuits, Vol. SC-27, No. 1, pp. 67–81, 1992.

    Google Scholar 

  9. O. Vermesan, The MOS Transistor as the Basic Building Block for Analog VLSI Implementation of Neural Networks. Scientific/Technical Report No. 1994-11, ISSN 0803-2696, University of Bergen, Norway, 1994.

    Google Scholar 

  10. O. Vermesan, Memory Units for Analog VLSI Implementation of Neural Networks. Scientific/Technical Report No. 1994-12, ISSN 0803-2696, University of Bergen, Norway, 1994.

    Google Scholar 

  11. O. Vermesan, Neural Networks Implementation — Issues and Techniques. Scientific/Technical Report No. 1994-20, ISSN 0803-2696, University of Bergen, Norway, 1994.

    Google Scholar 

  12. O. Vermesan, and A.I. Vermesan, “The Use of Hybrid Intelligent Systems in Telecommunications” In J. Liebowitz and D.S. Prerau, Eds., Worldwide Intelligent Systems-Approaches to Telecommunications and Network Management, Amsterdam, IOS Press, Chapter 10, pp. 186–226, 1995.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

José Mira Francisco Sandoval

Rights and permissions

Reprints and permissions

Copyright information

© 1995 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Vermesan, O. (1995). A modular VLSI architecture for neural networks implementation. In: Mira, J., Sandoval, F. (eds) From Natural to Artificial Neural Computation. IWANN 1995. Lecture Notes in Computer Science, vol 930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-59497-3_252

Download citation

  • DOI: https://doi.org/10.1007/3-540-59497-3_252

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-59497-0

  • Online ISBN: 978-3-540-49288-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics