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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

Xilu WANG

Xidian University

Yongjun SUN

Xidian University

Huaxi GU

Xidian University

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.

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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

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@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},}

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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 -