Artificial neural networks activation function HDL coder

Abstract

The sigmoid and hyperbolic tangent functions are usually used as the activation functions in Artificial Neural Networks (ANNs). The exponential nature of these functions make them difficult for hardware implementation. Hence, several different methods for approximating them in hardware are proposed. In this work, we present a MATLAB toolbox called the “SigTan HDL Coder”, that generates synthesizable HDL Code which approximates these functions in hardware according to the specific user requirements. The HDL code is platform independent and can be used for FPGA as well as ASIC implementations. Input parameters to the system are the approximation error, input range, and the approximation method. Three different user-selectable methods for approximating the functions are programmed in the toolbox. All implemented approximation methods avoid the use of multipliers for their implementation, as multipliers are expensive hardware components in terms of area and speed.