BMM: A Binary Metaheuristic Mapping Algorithm for Mesh-Based Network-on-Chip

Xilu WANG; Yongjun SUN; Huaxi GU

doi:10.1587/transinf.2018EDL8208

BMM: A Binary Metaheuristic Mapping Algorithm for Mesh-Based Network-on-Chip

Xilu WANG, Yongjun SUN, Huaxi GU

Full Text Views

0

Cite this

Summary :

The mapping optimization problem in Network-on-Chip (NoC) is constraint and NP-hard, and the deterministic algorithms require considerable computation time to find an exact optimal mapping solution. Therefore, the metaheuristic algorithms (MAs) have attracted great interests of researchers. However, most MAs are designed for continuous problems and suffer from premature convergence. In this letter, a binary metaheuristic mapping algorithm (BMM) with a better exploration-exploitation balance is proposed to solve the mapping problem. The binary encoding is used to extend the MAs to the constraint problem and an adaptive strategy is introduced to combine Sine Cosine Algorithm (SCA) and Particle Swarm Algorithm (PSO). SCA is modified to explore the search space effectively, while the powerful exploitation ability of PSO is employed for the global optimum. A set of well-known applications and large-scale synthetic cores-graphs are used to test the performance of BMM. The results demonstrate that the proposed algorithm can improve the energy consumption more significantly than some other heuristic algorithms.

Publication: IEICE TRANSACTIONS on Information Vol.E102-D No.3 pp.628-631

Publication Date: 2019/03/01

Publicized: 2018/11/26

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2018EDL8208

Type of Manuscript: LETTER

Category: Fundamentals of Information Systems

Authors

Xilu WANG
  Xidian University
Yongjun SUN
  Xidian University
Huaxi GU
  Xidian University

Keyword

network-on-chip, application mapping, particle swarm algorithm, sine cosine algorithm

Cite this

Copy

Xilu WANG, Yongjun SUN, Huaxi GU, "BMM: A Binary Metaheuristic Mapping Algorithm for Mesh-Based Network-on-Chip" in IEICE TRANSACTIONS on Information, vol. E102-D, no. 3, pp. 628-631, March 2019, doi: 10.1587/transinf.2018EDL8208.
Abstract: The mapping optimization problem in Network-on-Chip (NoC) is constraint and NP-hard, and the deterministic algorithms require considerable computation time to find an exact optimal mapping solution. Therefore, the metaheuristic algorithms (MAs) have attracted great interests of researchers. However, most MAs are designed for continuous problems and suffer from premature convergence. In this letter, a binary metaheuristic mapping algorithm (BMM) with a better exploration-exploitation balance is proposed to solve the mapping problem. The binary encoding is used to extend the MAs to the constraint problem and an adaptive strategy is introduced to combine Sine Cosine Algorithm (SCA) and Particle Swarm Algorithm (PSO). SCA is modified to explore the search space effectively, while the powerful exploitation ability of PSO is employed for the global optimum. A set of well-known applications and large-scale synthetic cores-graphs are used to test the performance of BMM. The results demonstrate that the proposed algorithm can improve the energy consumption more significantly than some other heuristic algorithms.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDL8208/_p

Copy

@ARTICLE{e102-d_3_628,
author={Xilu WANG, Yongjun SUN, Huaxi GU, },
journal={IEICE TRANSACTIONS on Information},
title={BMM: A Binary Metaheuristic Mapping Algorithm for Mesh-Based Network-on-Chip},
year={2019},
volume={E102-D},
number={3},
pages={628-631},
abstract={The mapping optimization problem in Network-on-Chip (NoC) is constraint and NP-hard, and the deterministic algorithms require considerable computation time to find an exact optimal mapping solution. Therefore, the metaheuristic algorithms (MAs) have attracted great interests of researchers. However, most MAs are designed for continuous problems and suffer from premature convergence. In this letter, a binary metaheuristic mapping algorithm (BMM) with a better exploration-exploitation balance is proposed to solve the mapping problem. The binary encoding is used to extend the MAs to the constraint problem and an adaptive strategy is introduced to combine Sine Cosine Algorithm (SCA) and Particle Swarm Algorithm (PSO). SCA is modified to explore the search space effectively, while the powerful exploitation ability of PSO is employed for the global optimum. A set of well-known applications and large-scale synthetic cores-graphs are used to test the performance of BMM. The results demonstrate that the proposed algorithm can improve the energy consumption more significantly than some other heuristic algorithms.},
keywords={},
doi={10.1587/transinf.2018EDL8208},
ISSN={1745-1361},
month={March},}

Copy

TY - JOUR
TI - BMM: A Binary Metaheuristic Mapping Algorithm for Mesh-Based Network-on-Chip
T2 - IEICE TRANSACTIONS on Information
SP - 628
EP - 631
AU - Xilu WANG
AU - Yongjun SUN
AU - Huaxi GU
PY - 2019
DO - 10.1587/transinf.2018EDL8208
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2019
AB - The mapping optimization problem in Network-on-Chip (NoC) is constraint and NP-hard, and the deterministic algorithms require considerable computation time to find an exact optimal mapping solution. Therefore, the metaheuristic algorithms (MAs) have attracted great interests of researchers. However, most MAs are designed for continuous problems and suffer from premature convergence. In this letter, a binary metaheuristic mapping algorithm (BMM) with a better exploration-exploitation balance is proposed to solve the mapping problem. The binary encoding is used to extend the MAs to the constraint problem and an adaptive strategy is introduced to combine Sine Cosine Algorithm (SCA) and Particle Swarm Algorithm (PSO). SCA is modified to explore the search space effectively, while the powerful exploitation ability of PSO is employed for the global optimum. A set of well-known applications and large-scale synthetic cores-graphs are used to test the performance of BMM. The results demonstrate that the proposed algorithm can improve the energy consumption more significantly than some other heuristic algorithms.
ER -