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

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Authors
Chowdhury, A.
Razali, M.
Wyai, G.
Gopal, Lenin
Madon, B.
Singh, A.
Date
2015
Type
Conference Paper

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Chowdhury, A. and Razali, M. and Wyai, G. and Gopal, L. and Madon, B. and Singh, A. 2015. An analysis of MVL neural operators using feed forward backpropagation: Realization and application of logic synthesis, International Conference on Smart Smart Sensors and Applications, pp. 122-126: IEEE.
Source Title
2015 International Conference on Smart Sensors and Application, ICSSA 2015
Source Conference
International Conference on Smart Smart Sensors and Applications
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Curtin Malaysia
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Abstract

© 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.