Advertisement

Fault Tolerance in Analog VLSI: Case Study of a Focal Plane Processor

  • A. G. Andreou
  • S. A. Kontogiorgis

Abstract

Biological systems provide good architectural models for information processing hardware. Difficult problems in machine perception and complex motor control are solved in a natural way by energy efficient and robust neural systems. Hopfield in his seminal paper [1] on physical systems with emergent computational abilities envisioned a new breed of integrated circuits that could implement such systems and would be much less sensitive to element failure than present day computers. Analog VLSI is a technology suitable for the implementation of synthetic neural systems [2, 3] on silicon.

Keywords

Tracking Error Fault Tolerance Bias Current Voltage Difference Illumination Intensity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    J.J. Hopfield, “Neural networks and physical systems with emergent computational abilities,” Proc. Nat. Acad. Sci. USA, vol. 79, pp. 2554–2558, 1982.MathSciNetCrossRefGoogle Scholar
  2. [2]
    C.A. Mead, Analog VLSI and Neural Systems, Reading, MA, Addison-Wesley, 1989.MATHCrossRefGoogle Scholar
  3. [3]
    A.G. Andreou and K.A. Boahen, “Synthetic neural systems using current-domain signal representations,” Neural Computation, vol.1, 4, 1989.Google Scholar
  4. [4]
    T. Poggio, V. Torre and C. Koch. “Computational vision and regularization theory,” Nature, vol. 317, pp. 314–319, 1985.CrossRefGoogle Scholar
  5. [5]
    W. Reichardt and T. Poggio, “Figure-Ground Discrimination by Relative Movement in the Visual System of the Fly,” Biological Cybernetics 35, 81–100, 1979.CrossRefGoogle Scholar
  6. [6]
    A.G. Andreou, K. Strohbehn and R.E. Jenkins, “A proposed scheme for the analog VLSI implementation of the Hassenstein-R.eichardt motion detector”, Presented at the 1989 Neural Networks for Computing conference. Snowbird; also Technical Report JHU/ECE-88/07, The Johns Hopkins University.Google Scholar
  7. [7]
    S.P. DeWeerth and C.A. Mead, “A Two-Dimensional Visual Tracking Array”, Proceedings of the 5th MIT Conference on Advanced Research on VLSI, J. Allen and F.T. Leighton eds. pp. 259-275. 1988.Google Scholar
  8. [8]
    M.A.C. Malier, S.P. DeWeerth et al., “Implementing Neural Architectures Using Analog VLSI Circuits,” IEEE Trans, on Circ. & Sys., Vol. 36, No. 5, pp. 643–652, May 1989.CrossRefGoogle Scholar
  9. [9]
    S.A. Kontogiorgis and A.G. Andreou, “Mathematical Analysis of an Analog VLSI Visual Tracking Array”, Technical Report JHU-CS-88/17, Johns Hopkins Univ.; S.A. Kontogiorgis and A.G. Andreou, “Performance Analysis of an Analog VLSI Light Beam Tracking Array”, Technical Report JHU-ECE-89/04, Johns Hopkins Univ.Google Scholar
  10. [10]
    S.A. Kontogiorgis and A.G. Andreou, “A Spatial Mean and Median Filter in Analog VLSI,” Proceedings of the 1989 Conference on Information Sciences and Systems, Baltimore, Maryland, 1989.Google Scholar

Copyright information

© Springer Science+Business Media New York 1990

Authors and Affiliations

  • A. G. Andreou
    • 1
  • S. A. Kontogiorgis
    • 1
  1. 1.Dept. of Electrical & Computer ScienceThe Johns Hopkins UniversityBaltimoreUSA

Personalised recommendations