International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169 Volume: 5 Issue: 6 1196 – 1198 _______________________________________________________________________________________________ 1196 IJRITCC | June 2017, Available @ http://www.ijritcc.org _______________________________________________________________________________________ VLSI Implementation of Modified Hamming Neural Network for non Binary Pattern Recognition S. Archana, ECE dept. BRECW Hyderabad, India archanasubhash2006@yahoo.co.in Dr. B. K. Madhavi, ECE dept, SWEC, Gandipet Hyderabad, India bkmadhavi2009@gmail.com Dr. I V Murlikrishna JNT University, Hyderabad , India iyyanki@gmail.com Abstract—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 Artificial Neural Network Architecture (ANN) with on chip learning in analog VLSI for pattern recognition. It is a maximum likelohood classifier which can be implemented using VLSI. Modified Hamming neural network architecture is presented.Thenew circuit is modified to accept real time inputs as well as to determine next close pattern with respect to input pattern.Modified digit recognition circuit was simulated using HSPICE level 49 model parameters with version 3.1180n at VDD of 3V. The circuit shows power consumption of 34mW and transient delay of 0.35nS. Keywords-VLSI, HSPICE, ANN, Hamming Neural Network, WTA __________________________________________________*****_________________________________________________ I. INTRODUCTION Neural network is an interconnected group of natural or artificial neuron that uses a mathemetical or computational model for information processing[1].Artificial neural network is widely used in wide variety of problems in the area of patter recognition,signalprocessing,telecommunications,medical technical diagnosis,robotics and control systems. ANN are implemented in digital,analog or mixed system architecture.Though ANN can be implemented on software for real time application software based ANN are slower in execution in comparison to hardware based ANN[2]. Hardware implementation of ANN is more popular due to high speed of operation,low power. ANN has following features:  Parallism: This makes neural network fast.  Fault tolerent: The disrtributed data processing of ANN makes it easy to include necessary redundancy to implement a fault.  Regular: ANN are composed of few different elements that are interconnected in a regular way. This makes the implementation easy[3].  Adaptive: By programming neural network can be made adaptable to new working condition.  Asynchronous: This is an advantage while implementing electrical circuitsbecause problem with spike on supply current worst case timing designs are eliminated[4]. Lippmann provides an introduction to the field of artificial neural networks by reviewing six important neural network models that can be used for pattern classification [5] as shown in figure 1. Figure 1 Neural Network models II. HAMMING NEURAL NETWORK It is a maximum likelohood classifier which selects from a group of exempler pattern the closest one to an input pattern[6].All patterns are presented in 1-D binary vector.When an input pattern P=(P1,P2,…Pn) is presented to the network,hamming network calculates matching score between input pattern and each exemplar pattern. Wi=(Wi1,Wi2,…Win) where 1≤≤ ܯ which is defined as ܯ = –  ,  =  −  −    =1 (1) where HD(Pi,Wi) is the hamming distance between input pattern P and exemplar pattern Wi. Pj is jth element of input pattern P(1≤≤) Wij is jth element of exemplar pattern Wi.After this WTA operation is done to the matching score where only one winner is indicated by high level output.  =  1  =1 0 ≠ 1 (2)