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

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.