Results from Pulse-Stream VLSI Neural Network Devices

  • Michael Brownlow
  • Lionel Tarassenko
  • Alan Murray


This paper describes a novel switched-capacitor design for the implementation of artificial neural networks in VLSI using the pulse-stream signalling mechanism and dynamic weight storage. Test results are presented from a small number of chips, paying particular attention to the synaptic weight linearity and storage time. The synaptic weights are fully-programmable and the VLSI chips can be used to process analogue sensor data in real time with an accuracy equivalent to 6 or 7 bits, as demonstrated in the robotics application described in the paper.


Neural Activity Synaptic Weight Clock Signal Neural State VLSI Chip 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Brownlow, M.J., Tarassenko, L. and Murray, A.F., “Analogue computation with VLSI neural network devices”, Electronics Letters, vol.26, pp. 1297–1299, 1990.CrossRefGoogle Scholar
  2. Graf, H.P., Jackel, L.D., Howard, R.E., Straughn, B., Denker, J.S., Hubbard, W.E., Tennant, D.M. and Schwartz, D., “VLSI implementation of a neural network memory with several hundreds of neurons ”, in Proc. AIP Conference on Neural Networks for Computing, Snowbird, pp. 182–187, 1986.Google Scholar
  3. Grossberg, S., “Some Physiological and Biochemical Consequences of Psychological Postulates”, Proc. Natl. Acad. Sci USA, pp. 758–765, 1968.Google Scholar
  4. Hamilton, A., Murray, A.F., Reekie, H.M. and Tarassenko, L., “Working Analogue Neural Network Chips”, in this volume.Google Scholar
  5. Hopfield, J.J., “Neural Networks and Physical Systems with Emergent Collective Computational Abilities”, Proc. Natl. Acad. Sci. USA,vol.79, pp.2554–2558, 1982.MathSciNetCrossRefGoogle Scholar
  6. Hopfield, J.J., “Neural Networks and Physical Systems with Graded Response have Collective Properties like those of Two-State Neurons”, Proc. Natl. Acad. Sci. USA, vol.81, pp. 3088–3092, 1984.CrossRefGoogle Scholar
  7. Murray, A.F. and Smith, A.V.W., “Asynchronous VLSI Neural Networks using Pulse Stream Arithmetic”, IEEE J. Solid-State Circuits & Systems, vol.23, pp. 688–697, 1988.CrossRefGoogle Scholar
  8. Murray, A.F., Smith, A.V.W. and Tarassenko, L., “Fully-programmable Analogue VLSI Devices for the Implementation of Neural Networks’, in VLSI for Artificial Intelligence, Delgado-Frias, J.G. and Moore, W.R., Eds., Kluwer Academic Publishers, Boston, Mass., pp. 236–244, 1989.CrossRefGoogle Scholar
  9. Murray, A.F., Hamilton, A. and Tarassenko, L., “Programmable Analog Pulse-firing Networks’, in Advances in Neural Information Processing Systems, Touretzky, D.S., Ed., Morgan Kaufmann, pp. 671–677, 1989.Google Scholar
  10. Tarassenko, L., Brownlow, M.J. and Murray, A.F., “VLSI neural networks for autonomous robot navigation”, in Proc. International Neural Network Conference,Paris, pp. 213–216,1990.Google Scholar
  11. Vittoz, E., Oguey, H., Maher, M.A., Nys, O., Dijkstra, E. and Chevroulet, M., “Analog Storage of Adjustable Synaptic Weights”, in Proceedings of 1st Int. Workshop on Microelectronics for Neural Networks, pp. 69–79, 1990.Google Scholar

Copyright information

© Springer Science+Business Media New York 1991

Authors and Affiliations

  • Michael Brownlow
    • 1
  • Lionel Tarassenko
    • 1
  • Alan Murray
    • 2
  1. 1.Department of Engineering ScienceOxford UniversityOxfordUK
  2. 2.Department of Electrical EngineeringEdinburgh UniversityUK

Personalised recommendations