NDLI: Using Recurrent Neural Networks for Circuit Complexity Modeling

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Table of contents

2005 Pakistan Section Multitopic Conference

8th International Multitopic Conference, 2004. Proceedings of INMIC 2004.

7th International Multi Topic Conference, 2003. INMIC 2003.

International Multi Topic Conference, 2002. Abstracts. INMIC 2002.

Proceedings. IEEE International Multi Topic Conference, 2001. IEEE INMIC 2001. Technology for the 21st Century.

Using Recurrent Neural Networks for Circuit Complexity Modeling

Content Provider	IEEE Xplore Digital Library
Author	Azam Beg Chandana Prasad, P.W. Arshad, M.M. Hasnain, K.
Copyright Year	2006
Description	Author affiliation: Coll. of Inf. Technol., United Arab Emirates Univ., Al-Ain (Azam Beg; Chandana Prasad, P.W.)
Abstract	Being able to model the complexity of Boolean functions in terms of number of nodes in a binary decision diagram can be quite useful in VLSI/CAD applications. Our investigation showed that it is possible to use the recurrent neural network (RNN) models for the prediction of circuit complexity. The modeling results matched closely with simulations with an average error of less than 1 %. The correlation coefficient between RNN's predictions and actual results for ISCAS benchmark circuits was 0.629.
Starting Page	194
Ending Page	197
File Size	3992743
Page Count	4
File Format	PDF
ISBN	142440794X
DOI	10.1109/INMIC.2006.358161
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-12-23
Publisher Place	Pakistan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Recurrent neural networks Complexity theory Boolean functions Neurons Neural networks Very large scale integration Educational institutions Predictive models Combinational circuits Information technology
Content Type	Text
Resource Type	Article

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