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Low-resource hardware implementation of the hyperbolic tangent for artificial neural networks

  • New applications of Artificial Neural Networks in Modeling & Control
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Abstract

Artificial neural networks are a widespread tool with application in a variety of areas ranging from the social sciences to engineering. Many of these applications have reached a hardware implementation phase and have been documented in scientific papers. Unfortunately, most of the implementations have a simplified hyperbolic tangent replacement which has been the most common problem, as well as the most resource-consuming block in terms of hardware. This paper proposes a low-resource hardware implementation of the hyperbolic tangent, by using the simplest solution in order to obtain the lowest error possible thus far with a set of 25 polynomials of third order, obtained with Chebyshev interpolations. The results obtained show that the solution proposed holds a low error while simultaneously promising the use of low resources, as only third-order polynomials are used.

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Acknowledgments

The authors would like to acknowledge the Portuguese Foundation for Science and Technology for their support in this work through the project PEst-OE/EEI/LA0009/2011.

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Authors and Affiliations

  1. Centro de Competências de Ciências Exactas e da Engenharia, Madeira Interactive Technologies Institute, Universidade da Madeira, Campus da Penteada, 9000-039, Funchal, Madeira, Portugal

    Darío Baptista & Fernando Morgado-Dias

Authors
  1. Darío Baptista
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  2. Fernando Morgado-Dias
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Corresponding author

Correspondence to Darío Baptista.

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Baptista, D., Morgado-Dias, F. Low-resource hardware implementation of the hyperbolic tangent for artificial neural networks. Neural Comput & Applic 23, 601–607 (2013). https://doi.org/10.1007/s00521-013-1407-x

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  • DOI: https://doi.org/10.1007/s00521-013-1407-x

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