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.


Neural Network Model Lookup Table Character Recognition Optical Character Recognition Input Noise 
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.


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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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