Analog VLSI Implementation of Feed Forward Neural Network for Signal Processing
Main Article Content
With the emergence of VLSI Technology in electronic industry, the numerous applications of integrated circuits in high-performance computing, consumer electronics, and telecommunications has been rising steadily, and at a very fast pace. Artificial intelligence is integral part of a neural network is based on mathematical equations and artificial neurons. The focus here is the implementation of the Neural Network Architecture (NNA) with on chip learning in analog VLSI for generic signal processing applications. The artificial neural network comprises of analog components like multipliers and adders along with the tan-sigmoid function generating circuit. The given architecture uses components such as Gilbert cell mixer (GCM), neuron activation function (NAF) to implement the functions an artificial neural network. With the balanced operation of the Gilbert cell clearer output is obtained by eliminating unwanted signals. The architecture is designed using 180nm CMOS/VLSI technology with Cadence virtuoso tool.
How to Cite
, P. D. S. N. I. G. “Analog VLSI Implementation of Feed Forward Neural Network for Signal Processing”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 3, no. 5, May 2015, pp. 3305-8, doi:10.17762/ijritcc.v3i5.4442.