Open Access iconOpen Access

ARTICLE

crossmark

Discrete Firefly Algorithm for Optimizing Topology Generation and Core Mapping of Network-on-Chip

S. Parvathi*, S. Umamaheswari

Department of Information Technology, Anna University, MIT Campus, Chennai, 600044, India

* Corresponding Author: S. Parvathi. Email: email

Intelligent Automation & Soft Computing 2022, 34(1), 15-32. https://doi.org/10.32604/iasc.2022.025290

Abstract

Network-on-chip (NoC) proves to be the best alternative to replace the traditional bus-based interconnection in Multi-Processor System on a Chip (MPSoCs). Irregular NoC topologies are highly recommended and utilised in various applications as they are application specific. Optimized mapping of the cores in a NoC plays a major role in its performance. Firefly algorithm is a bio-inspired meta-heuristic approach. Discretized firefly algorithm is used in our proposed work. In this work, two optimization algorithms are proposed: Topology Generation using Discrete Firefly Algorithm (TGDFA) and Core Mapping using Discrete Firefly Algorithm (CMDFA) for multimedia benchmark applications, Video Object Plane Decoder (VOPD), Multimedia Window Display (MWD) and MP3 Encoder. The irregular topology generated using TGDFA is mapped using CMDFA onto a reconfigurable mesh with switches in between the routers to make it a fault tolerant one. The proposed TGDFA provides better optimization of communication cost. The speed-up on the average run time of the tasks and the time taken to attain the best solution by the proposed TGDFA is significant. The dynamic energy consumption of core mapping obtained by the proposed CMDFA is lesser when compared to the existing work. Finally, the optimized core mapping is implemented in a cycle accurate NoC simulator: Noxim. It is substantiated experimentally that the proposed CMDFA outperforms the previous work.

Keywords


Cite This Article

S. Parvathi and S. Umamaheswari, "Discrete firefly algorithm for optimizing topology generation and core mapping of network-on-chip," Intelligent Automation & Soft Computing, vol. 34, no.1, pp. 15–32, 2022.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 1230

    View

  • 579

    Download

  • 0

    Like

Share Link