Design Methodology of Neural Network for Signal Processing

Abstract: There is various new & advance technologies in medical science we are trying to process the information artificially as our biological system performs inside our body. The main focus of the paper is on the implementation of Neural Network Architecture (NNA) with on chip learning in analog VLSI for generic signal processing applications. In the paper we used analog components like Gilbert Cell Multiplier (GCM), Neuron activation Function (NAF) are used to implement artificial NNA. Artificial intelligence through a biological word is realized based on mathematical equations and artificial neurons. The analog component is used for the compress of multipliers and adder in neural network , which is along with the tan-sigmoid function circuit using MOS transistor in sub threshold region. To trained the neural architecture we used the back propagation algorithm in analog domain with new techniques of weight storage. Layout design and verification of the proposed design is carried out using microwind3.1 software tool. The technology used in designing the layout is 45nm CMOS technology.

Keyword: Neural Network Architecture, Back Propagation Algorithm.