High Speed VLSI Implementation of the Hyperbolic Tangent Sigmoid Function

The hyperbolic tangent function is commonly used as the activation function in artificial neural networks. In this work two different hardware implementations for the hyperbolic tangent function are proposed. Both methods are based on the approximation of the function rather than calculating it, since it has exponential nature. The first method uses a lookup table to approximate the function, while the second method reduces the size of the table by using range addressable decoding as opposed to the classic decoding scheme. Hardware synthesis results show the proposed methods perform significantly faster, and use less area compared to other similar methods with the same amount of error.