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End-to-End Timing Analysis of Cause-Effect Chains in Automotive Embedded Systems
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-1276-3609
Robert Bosch GmbH, Renningen, Germany.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Arcticus Systems AB, Järfälla, Sweden.ORCID iD: 0000-0003-3242-6113
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-1687-930X
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2017 (English)In: Journal of systems architecture, ISSN 1383-7621, E-ISSN 1873-6165, Vol. 80, no Supplement C, 104-113 p.Article in journal (Refereed) Published
Abstract [en]

Automotive embedded systems are subjected to stringent timing requirements that need to be verified. One of the most complex timing requirement in these systems is the data age constraint. This constraint is specified on cause- effect chains and restricts the maximum time for the propagation of data through the chain. Tasks in a cause-effect chain can have different activation patterns and different periods, that introduce over- and under-sampling effects, which additionally aggravate the end-to-end timing analysis of the chain. Furthermore, the level of timing information available at various development stages (from modeling of the software architecture to the software implementation) varies a lot, the complete timing information is available only at the implementation stage. This uncertainty and limited timing information can restrict the end-to-end timing analysis of these chains. In this paper, we present methods to compute end-to-end delays based on different levels of system information. The characteristics of different communication semantics are further taken into account, thereby enabling timing analysis throughout the development process of such heterogeneous software systems. The presented methods are evaluated with extensive experiments. As a proof of concept, an industrial case study demonstrates the applicability of the proposed methods following a state-of-the-practice development process.

Place, publisher, year, edition, pages
2017. Vol. 80, no Supplement C, 104-113 p.
Keyword [en]
Data Propagation DelayAutomotive Real-Time
National Category
Embedded Systems
Identifiers
URN: urn:nbn:se:mdh:diva-37084DOI: 10.1016/j.sysarc.2017.09.004ISI: 000413883100010Scopus ID: 2-s2.0-85031742078OAI: oai:DiVA.org:mdh-37084DiVA: diva2:1153171
Projects
PREMISE - Predictable Multicore SystemsDPAC - Dependable Platforms for Autonomous systems and ControlPreView: Developing Predictable Vehicle Software on Multi-core
Available from: 2017-10-27 Created: 2017-10-27 Last updated: 2017-11-16Bibliographically approved
In thesis
1. Consolidating Automotive Real-Time Applications on Many-Core Platforms
Open this publication in new window or tab >>Consolidating Automotive Real-Time Applications on Many-Core Platforms
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Automotive systems have transitioned from basic transportation utilities to sophisticated systems. The rapid increase in functionality comes along with a steep increase in software complexity. This manifests itself in a surge of the number of functionalities as well as the complexity of existing functions. To cope with this transition, current trends shift away from today’s distributed architectures towards integrated architectures, where previously distributed functionality is consolidated on fewer, more powerful, computers. This can ease the integration process, reduce the hardware complexity, and ultimately save costs.

One promising hardware platform for these powerful embedded computers is the many-core processor. A many-core processor hosts a vast number of compute cores, that are partitioned on tiles which are connected by a Network-on-Chip. These natural partitions can provide exclusive execution spaces for different applications, since most resources are not shared among them. Hence, natural building blocks towards temporally and spatially separated execution spaces exist as a result of the hardware architecture.

Additionally to the traditional task local deadlines, automotive applications are often subject to timing constraints on the data propagation through a chain of semantically related tasks. Such requirements pose challenges to the system designer as they are only able to verify them after the system synthesis (i.e. very late in the design process).

In this thesis, we present methods that transform complex timing constraints on the data propagation delay to precedence constraints between individual jobs. An execution framework for the cluster of the many-core is proposed that allows access to cluster external memory while it avoids contention on shared resources by design. A partitioning and configuration of the Network-on-Chip provides isolation between the different applications and reduces the access time from the clusters to external memory. Moreover, methods that facilitate the verification of data propagation delays in each development step are provided. 

Place, publisher, year, edition, pages
Västerås: Malardalen University, 2017
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 246
Keyword
Many-Core, Automotive, Network-on-Chip, Real-Time, Timing analysis
National Category
Embedded Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-37182 (URN)978-91-7485-359-9 (ISBN)
Public defence
2017-12-19, Kappa, Mälardalens högskola, Västerås, 09:00 (English)
Opponent
Supervisors
Available from: 2017-11-06 Created: 2017-11-02 Last updated: 2017-11-06Bibliographically approved

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Becker, MatthiasMubeen, SaadBehnam, MorisNolte, Thomas

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