A Flexible Software Framework for Dynamic Task Allocation on MPSoCs Evaluated in an Automotive Context

Rettkowski, Jens; Wehner, Philipp; Schülper, Marc; Göhringer, Diana

doi:10.1007/978-3-319-16214-0_21

Cited by 2 publications

(1 citation statement)

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“…Todays's applications that run on embedded systems such as multimedia (Sanchez et al, 2015 ), automative context (Stoychev et al 2016;Rettkowski et al 2015), signal processing (Meloni et al 2015), and healthcare (Iranfar et al, 2018;El Mimouni & Karim 2014) have performance requirements which can no longer be satisfied by uniprocessor systems (Tafesse & Muthukumar, 2013). Heterogeneous Multiprocessor Systems-on-Chips (MPSoCs) are widely accepted as a solution for future embedded systems (Leupers et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Parallel Applications Mapping onto Network on Chip Based on Heterogeneous MPSoCs Using Hybrid Algorithms

Belkacemi

Daoui

Bouzefrane

et al. 2019

International Journal of Distributed Systems and Technologies

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Mapping parallel applications onto a network on chip (NoC) that is based on heterogeneous MPSoCs is considered as an instance of an NP-hard and a multi-objective problem. Various multi-objective algorithms have been proposed in the literature to handle this issue. Metaheuristics stand out as highly appropriate approaches to deal with this kind of problem. These metaheuristics are classified into two sets: population-based metaheuristics and single solution-based ones. To take advantage of the both sets, the trend is to use hybrid solutions that have shown to give better results. In this article, the authors propose to hybridize these two metaheuristics sets to find good Pareto mapping solutions to optimize the execution time and the energy consumption simultaneously. The experimental results have shown that the proposed hybrid algorithms give high quality non-dominated mapping solutions in a reasonable runtime.

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