Implementation of single neuron using various activation functions with FPGA

A main characteristic element of any artificial neural network (ANN) is an activation function since their results are used as a starting point for any complex ANN application. This paper is about design and implementation of single neuron with three different types of activation function sigmoid, linear and threshold activation function. Activation function package is written using VHDL and single precision floating point representation method is used to design the single neuron. The simulation result is taken using Xilinx ISE14.5i and the performance of different activation functions are analyzed and compared in terms of device utilization and CPU time.