A neural network implementation of hyperbolic tangent function using approximation method

Neural network is mainly used in faster applications. Nonlinear activation function is one of the main building blocks in the neural networks. Hyperbolic and tangent sigmoid function used in neural network. The approximation is based on a mathematical analysis considering the maximum allowable error as design parameter. To derive the optimal finding of the number of input and output bits required for hardware implementation of the proposed Approximation scheme. In the proposed structure bit level mapping, multiplexer and barrel shifter structure is presented. In the bit level mapping sequential logic circuit is used instead of combinational logic circuit for faster convergence. A barrel shifter is a digital circuit that can shift a data word by a specified number of bits in one clock cycle. It can be implemented as a sequence of multiplexers. To design an 8 bit input 5bit output neural network such as a pulse coupled neural network. This neural network is mainly used in image processing applications which are satellite imaging and medical imaging. Hardware synthesis results show that proposed methods perform significantly faster, and use less area compared to other similar methods with the same amount of error. The proposed architecture of this paper analysis the logic size, area and power consumption using Xilinx 14.2.