An All-Digital VLSI ANN

  • Brian White
  • Mohamed I. Elmasry


In recent years, there has been a great deal of research activity in artificial neural networks (ANN) in the area of simulation and hardware implementations [1, 2, 3, 4, 5, 6]. Because of the special features offered by ANNs, such as the capability to learn from examples, adaptation, parallelism, fault tolerance and noise resistance, they have been applied to a number of real-world problems including image and speech processing [3,6, 7, 8, 9]. To enhance the impact of ANNs and broaden the area of applications, it is imperative that ANNs benefit from the state-of-the-art VLSI and ULSI implementation technologies. Because these technologies are basically a digital implementation medium, ANNs must be adapted to an all-digital implementation approach. To illustrate this thesis, the research work reported in this paper offers a practical example of adapting an ANN model to an all-digital VLSI implementation.




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Copyright information

© Springer Science+Business Media New York 1994

Authors and Affiliations

  • Brian White
  • Mohamed I. Elmasry

There are no affiliations available

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