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Application mapping and NoC configuration using hybrid particle swarm optimization

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posted on 23.05.2021, 15:46 by Muhammad Obaidullah
Network-on-Chip (NoC) has been proposed as an interconnection framework for connecting large number of cores for a System-on-Chip (SoC). Assuming a mesh-based NoC, we investigate application mapping and NoC configuration optimization using a hybrid optimization scheme. Our technique, Hybrid Discrete Particle Swarm Optimization (HDPSO), combines Tabu-search, communication volume based core swapping, and swarm intelligence. We employ a Tabu-list to discourage swarm particles to re-visit the explored search space and propose an alternative route towards the intended movement direction. In each iteration of swarm, a sub-swarm containing configuration solutions (sub-particles) searches for optimal configuration for the parent particle (mapping solution). Optimization goals include minimum average communication latency, power, area, credit loop latency, and maximum average link duty factor. The proposed technique is tested for well-known multimedia application core graphs and several large synthetic cores-graphs. It was found that on average our hybrid scheme generates high quality NoC mapping and configuration solutions when compared to some existing stochastic optimization techniques.

History

Language

eng

Degree

Master of Applied Science

Program

Electrical and Computer Engineering

Granting Institution

Ryerson University

LAC Thesis Type

Thesis

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Electrical and Computer Engineering (Theses)

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