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

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

DOI

10.1109/ICSSA.2015.7322523

ISBN

9781479973644

School

Curtin Malaysia

URI

http://hdl.handle.net/20.500.11937/66742

Collection

Curtin Research Publications

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.