Optimization Strategy of Regular NoC Mapping Using Genetic-Based Hyper-Heuristic Algorithm

被引:2
作者
Xu, Changqing [1 ,2 ]
Ning, Jiahao [2 ]
Liu, Yi [2 ]
Luo, Mintao [3 ]
Chen, Dongdong [2 ]
Lin, Xiaoling [4 ]
Yang, Yintang [2 ]
机构
[1] Xidian Univ, Guangzhou Inst Technol, Guangzhou 510555, Peoples R China
[2] Xidian Univ, Sch Microelect, Xian 710071, Peoples R China
[3] Xian Microelect Technol, Xian 710054, Peoples R China
[4] China Elect Prod Reliabil & Environm Res, Guangzhou 510610, Peoples R China
来源
SYMMETRY-BASEL | 2022年 / 14卷 / 08期
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
network-on-chip; mapping; hyper-heuristic; isomorphic replacement crossover; symmetry; NETWORK;
D O I
10.3390/sym14081637
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Mapping optimization of network-on-chips (NoCs) for specific applications has become one of the most important keys of the SoC top-level design. However, the topology of NoC applied is usually regular topology, such as mesh, torus, etc., which may generate a large number of isomorphic solutions during the process of NoC mapping, which may reduce the convergence speed of mapping algorithms. In this paper, we proposed a generic-based hyper-heuristic algorithm named IRC-GHH for NoC mapping. To reduce the influence of isomorphic solutions, we analyzed the symmetry of NoC topology and proposed crossover operators based on the isomorphic solution to optimize the algorithm. We studied the situation of invalid crossovers and eliminated invalid iterations by adopting an isomorphic replacement crossover (IRC) strategy. To prevent the algorithm from falling into evolutionary stagnation in the late iteration, we introduce an adaptive mechanism to increase the usage frequency of the IRC operator automatically. Compared with GHH without IRC, the GHH with IRC can achieve, on average 15.25% communication energy reduction and 7.84% communication delay reduction.
引用
收藏
页数:12
相关论文
共 50 条
[41]   Hyper-Heuristic Optimization Using Multifeature Fusion Estimator for PCB Assembly Lines With Linear-Aligned-Heads Surface Mounters [J].
Lu, Guangyu ;
Gao, Huijun ;
Li, Zhengkai ;
Yu, Xinghu ;
Wang, Tong ;
Qiu, Jianbin ;
Rodriguez-Andina, Juan J. .
IEEE TRANSACTIONS ON CYBERNETICS, 2025, 55 (08) :3879-3890
[42]   An Estimation of Distribution Algorithm-Based Hyper-Heuristic for the Distributed Assembly Mixed No-Idle Permutation Flowshop Scheduling Problem [J].
Zhao, Fuqing ;
Zhu, Bo ;
Wang, Ling .
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2023, 53 (09) :5626-5637
[43]   A Q-learning based hyper-heuristic scheduling algorithm with multi-rule selection for sub-assembly in shipbuilding [J].
Wang, Teng ;
Zhang, Yahui ;
Hu, Xiaofeng .
COMPUTERS & INDUSTRIAL ENGINEERING, 2024, 197
[44]   A Hybrid Genetic Programming Hyper-Heuristic Approach for Online Two-level Resource Allocation in Container-based Clouds [J].
Tan, Boxiong ;
Ma, Hui ;
Mei, Yi .
2019 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2019, :2681-2688
[45]   Deadline, Energy and Buffer-Aware Task Mapping Optimization in NoC-based SoCs using Genetic Algorithms [J].
Bruch, J. V. ;
da Silva, E. A. ;
Zeferino, C. A. ;
Indrusiak, L. S. .
2017 VII BRAZILIAN SYMPOSIUM ON COMPUTING SYSTEMS ENGINEERING (SBESC), 2017, :86-93
[46]   Genetic programming-based hyper-heuristic approach for solving dynamic job shop scheduling problem with extended technical precedence constraints [J].
Fan, Huali ;
Xiong, Hegen ;
Goh, Mark .
COMPUTERS & OPERATIONS RESEARCH, 2021, 134
[47]   Q-learning based hyper-heuristic algorithm for solving multi-mode resource-constrained project scheduling problem [J].
Cui J. ;
Lyu Y. ;
Xu Z. .
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS, 2022, 28 (05) :1472-1481
[48]   An adaptive genetic hyper-heuristic algorithm for a two-echelon vehicle routing problem with dual-customer satisfaction in community group-buying [J].
Xu, Song ;
Ou, Xiangyue ;
Govindan, Kannan ;
Chen, Mingzhou ;
Yang, Wenting .
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW, 2025, 194
[49]   A Q-learning-based hyper-heuristic evolutionary algorithm for the distributed flexible job-shop scheduling problem with crane transportation [J].
Zhang, Zi-Qi ;
Wu, Fang-Chun ;
Qian, Bin ;
Hu, Rong ;
Wang, Ling ;
Jin, Huai-Ping .
EXPERT SYSTEMS WITH APPLICATIONS, 2023, 234
[50]   Energy and thermal aware mapping for NoC using non-dominated sorting genetic algorithm II [J].
Zang, Mingxiang ;
Ye, Qiuju ;
Liu, Huimi .
Journal of Information and Computational Science, 2015, 12 (01) :201-209