The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
BDD(이진 결정 다이어그램)는 VLSI CAD 도구를 사용하여 디지털 회로를 설계하는 데 중요한 데이터 구조입니다. 변수의 순서는 BDD의 총 노드 수와 경로 길이에 영향을 미칩니다. 좋은 변수 순서를 찾는 것은 최적화 문제이며 이전에는 여러 연구 작업에서 BDD에 대해 많은 최적화 접근 방식이 구현되었습니다. 본 논문에서는 노드 수와 최장 경로 길이를 목표로 하는 BDD 변수 순서 문제에 대해 SMO(Spider Monkey Optimization) 알고리즘을 기반으로 한 최적화 접근 방식을 제안합니다. SMO는 거미 원숭이의 먹이 활동을 기반으로 하는 잘 알려진 군집 지능 기반 최적화 접근 방식입니다. 제안된 작업은 PSO(Particle Swarm Optimization) 알고리즘을 사용하는 다른 최신 BDD 재정렬 접근 방식과 비교되었습니다. 얻은 결과는 Particle Swarm Optimization 방법에 비해 상당한 개선을 보여줍니다. 제안된 SMO 기반 방법은 서로 다른 수준의 복잡성을 갖는 서로 다른 벤치마크 디지털 회로에 적용됩니다. 테스트된 최대 회로 수에 대한 노드 수와 가장 긴 경로 길이는 PSO보다 SMO에서 더 나은 것으로 나타났습니다.
Mohammed BALAL SIDDIQUI
Jamia Millia Islamia
Mirza TARIQ BEG
Jamia Millia Islamia
Syed NASEEM AHMAD
Jamia Millia Islamia
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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
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@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},}
부
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 -