Implementation of Knowledege Based Neural Network with Hyperbolic Tanget Function

Neural networks have a wide range of applications in analog and digital signal processing Nonlinear activation function is one of the main building blocks of artificial neural networks. Hyperbolic tangent and sigmoid are the most used nonlinear activation functions of NN.This project proposes a knowledge-based neural network (KBNN) modeling approach with new hyperbolic tangent function . The KBNN embeds the existing FPGA analytical models (AMs) into an NN.For fast computation of neuron in NN we use new approximation scheme for hyperbolic tangent function calculation . The NN can complement the AMs according to their needs to provide further increased model accuracy, while maintaining the meaningful trends successfully captured in the AMs. The obtained KBNN predicts the routing channel width required by circuit implementations on various FPGA architectures , which can be used by architects to quickly and accurately evaluate various FPGA architectures in early development stages.the proposed KBNN coded using verilogHDL and simulated using Xilinx 12.1. the proposed KBNN with new activation function results in reduction in area, delay, and power in VLSI implementation of artificial neural networks with hyperbolic tangent activation function.