An Optimized Nature-Inspired Metaheuristic Algorithm for Application Mapping in 2D-NoC
Mapping application task graphs on intellectual property (IP) cores into network-on-chip (NoC) is a non-deterministic polynomial-time hard problem. The evolution of network performance mainly depends on an effective and efficient mapping technique and the optimization of performance and cost metrics. These metrics mainly include power, reliability, area, thermal distribution and delay. A state-of-the-art mapping technique for NoC is introduced with the name of sailfish optimization algorithm (SFOA). The proposed algorithm minimizes the power dissipation of NoC via an empirical base applying a shared k-nearest neighbor clustering approach, and it gives quicker mapping over six considered standard benchmarks. The experimental results indicate that the proposed techniques outperform other existing nature-inspired metaheuristic approaches, especially in large application task graphs.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:21 |
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| Enthalten in: |
Sensors (Basel, Switzerland) - 21(2021), 15 vom: 28. Juli |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Sikandar, Saleha [VerfasserIn] |
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| Links: |
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| Themen: |
Journal Article |
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| Anmerkungen: |
Date Revised 03.04.2024 published: Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.3390/s21155102 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM329164473 |
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| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a Mapping application task graphs on intellectual property (IP) cores into network-on-chip (NoC) is a non-deterministic polynomial-time hard problem. The evolution of network performance mainly depends on an effective and efficient mapping technique and the optimization of performance and cost metrics. These metrics mainly include power, reliability, area, thermal distribution and delay. A state-of-the-art mapping technique for NoC is introduced with the name of sailfish optimization algorithm (SFOA). The proposed algorithm minimizes the power dissipation of NoC via an empirical base applying a shared k-nearest neighbor clustering approach, and it gives quicker mapping over six considered standard benchmarks. The experimental results indicate that the proposed techniques outperform other existing nature-inspired metaheuristic approaches, especially in large application task graphs | ||
| 650 | 4 | |a Journal Article | |
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| 700 | 1 | |a Hussain, Fawad |e verfasserin |4 aut | |
| 700 | 1 | |a Amin, Waqar |e verfasserin |4 aut | |
| 700 | 1 | |a Zikria, Yousaf Bin |e verfasserin |4 aut | |
| 700 | 1 | |a Yu, Heejung |e verfasserin |4 aut | |
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