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A 2-layer Component-based Allocation for Embedded Systems with GPUs
Bombardier Transportation, Västerås, Sweden.ORCID iD: 0000-0001-9794-5497
RISE SICS Västerås, Sweden.ORCID iD: 0000-0002-1512-0844
2019 (English)In: Designs, ISSN 2411-9660, Vol. 3, p. 1-14Article in journal (Refereed) Published
Abstract [en]

Component-based development is a software engineering paradigm that can facilitate the construction of embedded systems and tackle its complexities. The modern embedded systems have more and more demanding requirements. One way to cope with such versatile and growing set of requirements is to employ heterogeneous processing power, i.e., CPU-GPU architectures. The new CPU-GPU embedded boards deliver an increased performance but also introduce additional complexity and challenges. In this work, we address the component-to-hardware allocation for CPU-GPU embedded systems. The allocation for such systems is much complex due to the increased amount of GPU-related information. For example, while in traditional embedded systems the allocation mechanism may consider only the CPU memory usage of components to find an appropriate allocation scheme, in heterogeneous systems, the GPU memory usage needs also to be taken into account in the allocation process. This paper aims at decreasing the component-to-hardware allocation complexity by introducing a 2-layer component-based architecture for heterogeneous embedded systems. The detailed CPU-GPU information of the system is abstracted at a high-layer by compacting connected components into single units that behave as regular components. The allocator, based on the compacted information received from the high-level layer, computes, with a decreased complexity, feasible allocation schemes. In the last part of the paper, the 2-layer allocation method is evaluated using an existing embedded system demonstrator; namely, an underwater robot.

Place, publisher, year, edition, pages
Switzerland, 2019. Vol. 3, p. 1-14
National Category
Engineering and Technology Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-43913ISRN: 2411-9660DOI: 10.3390/designs3010006OAI: oai:DiVA.org:mdh-43913DiVA, id: diva2:1323127
Projects
TOCSYC - Testing of Critical System Characteristics (KKS)DPAC - Dependable Platforms for Autonomous systems and ControlAvailable from: 2019-06-11 Created: 2019-06-11 Last updated: 2019-06-11Bibliographically approved

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Campeanu, GabrielSaadatmand, Mehrdad

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CiteExportLink to record
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  • apa
  • ieee
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