An analysis of MVL neural operators using feed forward backpropagation: Realization and application of logic synthesis

© 2015 IEEE. In this paper, a Neural Network Deployment (NND) algorithm is presented to realize and synthesize Multi-Valued Logic (MVL) functions. The algorithm is combined with back-propagation learning capability and MVL operators. The operators are used to synthesize the functions. Consequently t...

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Bibliographic Details
Main Authors: Chowdhury, A., Razali, M., Wyai, G., Gopal, Lenin, Madon, B., Singh, A.
Format: Conference Paper
Published: IEEE 2015
Online Access:http://hdl.handle.net/20.500.11937/66742
Description
Summary:© 2015 IEEE. In this paper, a Neural Network Deployment (NND) algorithm is presented to realize and synthesize Multi-Valued Logic (MVL) functions. The algorithm is combined with back-propagation learning capability and MVL operators. The operators are used to synthesize the functions. Consequently the synthesized expressions are applied by the MVL neural operators. The advantages of NND-MVL algorithm are demonstrated by accuracy measurement of MVL neural operator realization. Furthermore, evaluation of NND-MVL algorithm is analyzed by its application, propagation delay and accuracy achieved for training with 4 hidden neurons. In a brief, an effort of training MVL neural operators and utilizing them for logic synthesis is observed.