%0 Journal Article
%J Neural Computing and Applications
%D 2013
%T Low-resource hardware implementation of the hyperbolic tangent for artificial neural networks
%A Dario Baptista
%A Morgado, Dias F.
%K Artificial Neural Networks
%K Function approximation
%K Hyperbolic tangent
%K Polynomial approximation
%X 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.
%B Neural Computing and Applications
%I Springer-Verlag
%P 1-7
%G eng
%U http://dx.doi.org/10.1007/s00521-013-1407-x