Abstract
Deploying software to embedded multi- and many-core hardware has become increasingly complex in the past years. Due to the heterogeneous nature of embedded systems and the complex underlying Network on Chip structures of many-core architectures, aspects such as the runtime of executable software are highly influenced by a variety of factors, e.g. the type, instruction set, and speed of the processor an executable is allocated to as well as its predecessors, their location, ordering and the communication channels in between them. In this work, we propose a semi-automated Hybrid Genetic Algorithm based optimization approach for distributing and re-scheduling executional software to heterogeneous hardware architectures in constrained solution spaces, along with an evaluation of its applicability and efficiency. The evaluation is based on both, publicly available as well as real world examples of automotive engine management systems.
The research leading to these results has received funding from the Federal Ministry for Education and Research (BMBF) under Grant 01|S14029K in the context of the ITEA3 EU-Project AMALTHEA4public.
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Cuadra, P., Krawczyk, L., Höttger, R., Heisig, P., Wolff, C. (2017). Automated Scheduling for Tightly-Coupled Embedded Multi-core Systems Using Hybrid Genetic Algorithms. In: Damaševičius, R., Mikašytė, V. (eds) Information and Software Technologies. ICIST 2017. Communications in Computer and Information Science, vol 756. Springer, Cham. https://doi.org/10.1007/978-3-319-67642-5_30
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DOI: https://doi.org/10.1007/978-3-319-67642-5_30
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