Meng Yang
Fast tabular based conversion methods for Canonical OR-Coincidence.
Yang, Meng; Wang, P; Chen, X; Almaini, A E A
Authors
P Wang
X Chen
A E A Almaini
Abstract
Two fast conversion alogorithms based on tabular technique for Canonical OR-Coincidence (COC) expansions are introduced. By using bitwise operations, the Serial Tabular Technique (STT) can achieve speed of less than 2 seconds for 21 variables for randomly generated functions. The other proposed Fast Tabular Technique (FPTT) generates new terms in parallel instead of one variable at a time and achieves fast conversion speed of less than 6 seconds for 25 variables for sparse functions. Inverse method is proposed to improve the conversion speed of STT and FPTT when the number of maxterms is greater than 2 [to the nth]/2. The experimental results obtained by STT and FPTT are also compared to those in the literature. Our results outperform those significantly in all cases and could achieve less than 0.2 seconds for IWLS benchmark up to 17 variables.
Citation
Yang, M., Wang, P., Chen, X., & Almaini, A. E. A. (2005). Fast tabular based conversion methods for Canonical OR-Coincidence. In EUROCON 2005 - The International Conference on Computer as a Tool (507-510). https://doi.org/10.1109/EURCON.2005.1629976
Start Date | Nov 21, 2005 |
---|---|
End Date | Nov 24, 2005 |
Publication Date | Nov 21, 2005 |
Deposit Date | May 14, 2009 |
Peer Reviewed | Peer Reviewed |
Volume | 1 |
Pages | 507-510 |
Book Title | EUROCON 2005 - The International Conference on Computer as a Tool |
ISBN | 9781424400492 |
DOI | https://doi.org/10.1109/EURCON.2005.1629976 |
Keywords | Computer logic; Parallel programming; Canonical OR-Coincidence; Tabular conversion; Conversion speed; |
Public URL | http://researchrepository.napier.ac.uk/id/eprint/2595 |
Publisher URL | http://dx.doi.org/10.1109/EURCON.2005.1629976 |
Downloadable Citations
About Edinburgh Napier Research Repository
Administrator e-mail: repository@napier.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search