Network Partitioning Domain Knowledge Multiobjective Application Mapping for Large-Scale Network-on-Chip
This paper proposes a multiobjective application mapping technique targeted for large-scale network-on-chip (NoC). As the number of intellectual property (IP) cores in multiprocessor system-on-chip (MPSoC) increases, NoC application mapping to find optimum core-to-topology mapping becomes more chall...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
|
| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2014/867612 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850157313463156736 |
|---|---|
| author | Yin Zhen Tei Yuan Wen Hau N. Shaikh-Husin M. N. Marsono |
| author_facet | Yin Zhen Tei Yuan Wen Hau N. Shaikh-Husin M. N. Marsono |
| author_sort | Yin Zhen Tei |
| collection | DOAJ |
| description | This paper proposes a multiobjective application mapping technique targeted for large-scale network-on-chip (NoC). As the number of intellectual property (IP) cores in multiprocessor system-on-chip (MPSoC) increases, NoC application mapping to find optimum core-to-topology mapping becomes more challenging. Besides, the conflicting cost and performance trade-off makes multiobjective application mapping techniques even more complex. This paper proposes an application mapping technique that incorporates domain knowledge into genetic algorithm (GA). The initial population of GA is initialized with network partitioning (NP) while the crossover operator is guided with knowledge on communication demands. NP reduces the large-scale application mapping complexity and provides GA with a potential mapping search space. The proposed genetic operator is compared with state-of-the-art genetic operators in terms of solution quality. In this work, multiobjective optimization of energy and thermal-balance is considered. Through simulation, knowledge-based initial mapping shows significant improvement in Pareto front compared to random initial mapping that is widely used. The proposed knowledge-based crossover also shows better Pareto front compared to state-of-the-art knowledge-based crossover. |
| format | Article |
| id | doaj-art-3934d4f2bb624c2dbf16dff29043f7e9 |
| institution | OA Journals |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-3934d4f2bb624c2dbf16dff29043f7e92025-08-20T02:24:13ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322014-01-01201410.1155/2014/867612867612Network Partitioning Domain Knowledge Multiobjective Application Mapping for Large-Scale Network-on-ChipYin Zhen Tei0Yuan Wen Hau1N. Shaikh-Husin2M. N. Marsono3Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, MalaysiaFaculty of Biosciences and Medical Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, MalaysiaFaculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, MalaysiaFaculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, MalaysiaThis paper proposes a multiobjective application mapping technique targeted for large-scale network-on-chip (NoC). As the number of intellectual property (IP) cores in multiprocessor system-on-chip (MPSoC) increases, NoC application mapping to find optimum core-to-topology mapping becomes more challenging. Besides, the conflicting cost and performance trade-off makes multiobjective application mapping techniques even more complex. This paper proposes an application mapping technique that incorporates domain knowledge into genetic algorithm (GA). The initial population of GA is initialized with network partitioning (NP) while the crossover operator is guided with knowledge on communication demands. NP reduces the large-scale application mapping complexity and provides GA with a potential mapping search space. The proposed genetic operator is compared with state-of-the-art genetic operators in terms of solution quality. In this work, multiobjective optimization of energy and thermal-balance is considered. Through simulation, knowledge-based initial mapping shows significant improvement in Pareto front compared to random initial mapping that is widely used. The proposed knowledge-based crossover also shows better Pareto front compared to state-of-the-art knowledge-based crossover.http://dx.doi.org/10.1155/2014/867612 |
| spellingShingle | Yin Zhen Tei Yuan Wen Hau N. Shaikh-Husin M. N. Marsono Network Partitioning Domain Knowledge Multiobjective Application Mapping for Large-Scale Network-on-Chip Applied Computational Intelligence and Soft Computing |
| title | Network Partitioning Domain Knowledge Multiobjective Application Mapping for Large-Scale Network-on-Chip |
| title_full | Network Partitioning Domain Knowledge Multiobjective Application Mapping for Large-Scale Network-on-Chip |
| title_fullStr | Network Partitioning Domain Knowledge Multiobjective Application Mapping for Large-Scale Network-on-Chip |
| title_full_unstemmed | Network Partitioning Domain Knowledge Multiobjective Application Mapping for Large-Scale Network-on-Chip |
| title_short | Network Partitioning Domain Knowledge Multiobjective Application Mapping for Large-Scale Network-on-Chip |
| title_sort | network partitioning domain knowledge multiobjective application mapping for large scale network on chip |
| url | http://dx.doi.org/10.1155/2014/867612 |
| work_keys_str_mv | AT yinzhentei networkpartitioningdomainknowledgemultiobjectiveapplicationmappingforlargescalenetworkonchip AT yuanwenhau networkpartitioningdomainknowledgemultiobjectiveapplicationmappingforlargescalenetworkonchip AT nshaikhhusin networkpartitioningdomainknowledgemultiobjectiveapplicationmappingforlargescalenetworkonchip AT mnmarsono networkpartitioningdomainknowledgemultiobjectiveapplicationmappingforlargescalenetworkonchip |