An priority based weighted neuron net VLSI implementation

Abstract

Neural Networks (NN) 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 neural activation functions of NN. This project proposes a knowledge-based neural network (KBNN) modeling approach with new hyperbolic tangent function using Hashing trick. The KBNN embeds the existing FPGA analytical models (AM) into an NN. For fast computation of neuron in NN, we use new approximation scheme with hashing algorithm for the hyperbolic tangent function calculation. Hashing trick algorithm eliminates the less weight and using average weighting function. The approximation is based on mathematical analysis considering the maximum allowable error as design parameter. The NN can complement the analytical models according to their needs to provide further increased model accuracy, while maintaining the meaningful trends successfully captured in the analytical models. The proposed KBNN coded using verilog HDL and simulated using Xilinx 12.1. Also the proposed KBNN with new activation function and hashing trick method results in reduction of number of multiplications, area, delay, computation cost and power in VLSI implementation of artificial neural networks with hyperbolic tangent activation function.