Application Task Mapping in Real Time Network on Chip Systems for Latency Optimization Using Bio Inspired Greedy Firefly Algorithm
| International Journal of Electrical and Electronics Engineering |
| © 2024 by SSRG - IJEEE Journal |
| Volume 11 Issue 11 |
| Year of Publication : 2024 |
| Authors : Shweta Ashtekar, Kushal Tuckley |
10.14445/23488379/IJEEE-V11I11P128
How to Cite?
Shweta Ashtekar, Kushal Tuckley, "Application Task Mapping in Real Time Network on Chip Systems for Latency Optimization Using Bio Inspired Greedy Firefly Algorithm," SSRG International Journal of Electrical and Electronics Engineering, vol. 11, no. 11, pp. 295-305, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I11P128
Abstract:
Network on Chip (NoC) provides a communication framework within multiple cores in a heterogeneous computing ecosystem. While the execution of real time embedded applications like multimedia, data networks, and signal processing, mapping application tasks in NoC on appropriate cores is the most crucial, affecting overall performance and latency. This research proposes a nature-inspired metaheuristic Greedy Firefly Algorithm (GFF) for NoC, which combines the greedy approach with the firefly algorithm for mapping tasks. It is examined against three existing algorithms: NMAP, BB and Random algorithm using identical embedded traffic scenarios and simulation environment to establish the aptness of the suggested algorithm. The results of the GFF algorithm prove more efficient at higher traffic loads for applications such as PIP, MWD, CAVLC, MMS, VOPD, and E3S Consumer benchmarks and reduces average latency by almost 5 to 20% as well as increased throughput compared to other algorithms and is significant in critical applications. The simulator's generated dataset was subjected to an SVM ML model, which predicts how GFF is appropriate for the mentioned applications while considering minimal latency.
Keywords:
Network on chip, Application task mapping, Real time embedded applications, Metaheuristic, Nature inspired, Greedy Firefly algorithm (GFF), Latency optimization, SVM.
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