Implementation of Fixed-point Neuron Models with Threshold, Ramp and Sigmoid Activation Functions

This paper presents the hardware implementation of single-neuron models with three types of activation functions using fixed-point data format on Field Programmable Gate Arrays (FPGA). Activation function defines the transfer behavior of a neuron model and consequently the Artificial Neural Network (ANN) constructed using it. This paper compared single neuron models designed with bipolar ramp, threshold and sigmoid activation functions. It is also demonstrated that the FPGA hardware implementation performance can be significantly improved by using 16-bit fixed-point data format instead of 32-bit floating-point data format for the neuron model with sigmoid activation function.