IEICE TRANSACTIONS on Fundamentals
Variable Ordering in Binary Decision Diagram Using Spider Monkey Optimization for Node and Path Length Optimization
Summary :
Binary Decision Diagrams (BDDs) are an important data structure for the design of digital circuits using VLSI CAD tools. The ordering of variables affects the total number of nodes and path length in the BDDs. Finding a good variable ordering is an optimization problem and previously many optimization approaches have been implemented for BDDs in a number of research works. In this paper, an optimization approach based on Spider Monkey Optimization (SMO) algorithm is proposed for the BDD variable ordering problem targeting number of nodes and longest path length. SMO is a well-known swarm intelligence-based optimization approach based on spider monkeys foraging behavior. The proposed work has been compared with other latest BDD reordering approaches using Particle Swarm Optimization (PSO) algorithm. The results obtained show significant improvement over the Particle Swarm Optimization method. The proposed SMO-based method is applied to different benchmark digital circuits having different levels of complexities. The node count and longest path length for the maximum number of tested circuits are found to be better in SMO than PSO.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E106-A No.7 pp.976-989
- Publication Date
- 2023/07/01
- Publicized
- 2023/01/16
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.2021EAP1108
- Type of Manuscript
- PAPER
- Category
- VLSI Design Technology and CAD
Authors
Mohammed BALAL SIDDIQUI
Jamia Millia Islamia
Mirza TARIQ BEG
Jamia Millia Islamia
Syed NASEEM AHMAD
Jamia Millia Islamia
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Mohammed BALAL SIDDIQUI, Mirza TARIQ BEG, Syed NASEEM AHMAD, "Variable Ordering in Binary Decision Diagram Using Spider Monkey Optimization for Node and Path Length Optimization" in IEICE TRANSACTIONS on Fundamentals,
vol. E106-A, no. 7, pp. 976-989, July 2023, doi: 10.1587/transfun.2021EAP1108.
Abstract: Binary Decision Diagrams (BDDs) are an important data structure for the design of digital circuits using VLSI CAD tools. The ordering of variables affects the total number of nodes and path length in the BDDs. Finding a good variable ordering is an optimization problem and previously many optimization approaches have been implemented for BDDs in a number of research works. In this paper, an optimization approach based on Spider Monkey Optimization (SMO) algorithm is proposed for the BDD variable ordering problem targeting number of nodes and longest path length. SMO is a well-known swarm intelligence-based optimization approach based on spider monkeys foraging behavior. The proposed work has been compared with other latest BDD reordering approaches using Particle Swarm Optimization (PSO) algorithm. The results obtained show significant improvement over the Particle Swarm Optimization method. The proposed SMO-based method is applied to different benchmark digital circuits having different levels of complexities. The node count and longest path length for the maximum number of tested circuits are found to be better in SMO than PSO.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2021EAP1108/_p
Copy
@ARTICLE{e106-a_7_976,
author={Mohammed BALAL SIDDIQUI, Mirza TARIQ BEG, Syed NASEEM AHMAD, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Variable Ordering in Binary Decision Diagram Using Spider Monkey Optimization for Node and Path Length Optimization},
year={2023},
volume={E106-A},
number={7},
pages={976-989},
abstract={Binary Decision Diagrams (BDDs) are an important data structure for the design of digital circuits using VLSI CAD tools. The ordering of variables affects the total number of nodes and path length in the BDDs. Finding a good variable ordering is an optimization problem and previously many optimization approaches have been implemented for BDDs in a number of research works. In this paper, an optimization approach based on Spider Monkey Optimization (SMO) algorithm is proposed for the BDD variable ordering problem targeting number of nodes and longest path length. SMO is a well-known swarm intelligence-based optimization approach based on spider monkeys foraging behavior. The proposed work has been compared with other latest BDD reordering approaches using Particle Swarm Optimization (PSO) algorithm. The results obtained show significant improvement over the Particle Swarm Optimization method. The proposed SMO-based method is applied to different benchmark digital circuits having different levels of complexities. The node count and longest path length for the maximum number of tested circuits are found to be better in SMO than PSO.},
keywords={},
doi={10.1587/transfun.2021EAP1108},
ISSN={1745-1337},
month={July},}
Copy
TY - JOUR
TI - Variable Ordering in Binary Decision Diagram Using Spider Monkey Optimization for Node and Path Length Optimization
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 976
EP - 989
AU - Mohammed BALAL SIDDIQUI
AU - Mirza TARIQ BEG
AU - Syed NASEEM AHMAD
PY - 2023
DO - 10.1587/transfun.2021EAP1108
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E106-A
IS - 7
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - July 2023
AB - Binary Decision Diagrams (BDDs) are an important data structure for the design of digital circuits using VLSI CAD tools. The ordering of variables affects the total number of nodes and path length in the BDDs. Finding a good variable ordering is an optimization problem and previously many optimization approaches have been implemented for BDDs in a number of research works. In this paper, an optimization approach based on Spider Monkey Optimization (SMO) algorithm is proposed for the BDD variable ordering problem targeting number of nodes and longest path length. SMO is a well-known swarm intelligence-based optimization approach based on spider monkeys foraging behavior. The proposed work has been compared with other latest BDD reordering approaches using Particle Swarm Optimization (PSO) algorithm. The results obtained show significant improvement over the Particle Swarm Optimization method. The proposed SMO-based method is applied to different benchmark digital circuits having different levels of complexities. The node count and longest path length for the maximum number of tested circuits are found to be better in SMO than PSO.
ER -
English
