Hardware implementation of neural network with Sigmoidal activation functions using CORDIC

Abstract

Activation function is the most important function in neural network processing. In this article, the field-programmable gate array (FPGA)-based hardware implementation of a multilayer feed-forward neural network, with a log sigmoid activation function and a tangent sigmoid (hyperbolic tangent) activation function has been presented, with more accuracy than any other previous implementation of a neural network with the same activation function. Accuracy is enhanced through the implementation of both the sigmoidal functions using COordinate Rotation DIgital Computer (CORDIC) algorithm. The CORDIC algorithm is a simple and effective method for calculation of the trigonometric and hyperbolic functions. Simulations and experiments have been performed on the ISim simulation engine of the Xilinx Framework, using the Very High Speed Integrated Circuit Hardware Description Language (VHDL) as the programming language. The results show accuracy for a 32-bit and 64-bit input/output, compromising with speed.