Special Session: Future Automotive Systems Design: Research Challenges and Opportunities

Saidi, Selma; Steinhorst, Sebastian; Hamann, Arne; Ziegenbein, Dirk; Wolf, Martin

doi:10.1109/codesisss.2018.8525873

Cited by 42 publications

(31 citation statements)

References 10 publications

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“…Using C++, we simulate a fully connected heterogeneous 32-processor system on a laptop with 16 GB of Memory and an Inter core i7 processor. Parameters of the processors are randomly set in the following ranges: [1,3], which reflect the realworld characteristics, such as for Intel Mobile Pentium III and ARM Cortex-A9. It is to be noted that redundancy, which can be deployed to achieve extremely high reliability, is not considered in this work.…”

Section: B Experimental Platform and Dag Applicationsmentioning

confidence: 99%

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Dynamic DAG Scheduling on Multiprocessor Systems: Reliability, Energy, and Makespan

Huang

Jiao

et al. 2020

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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Multiprocessor systems are increasingly deployed in real-time applications, where reliability, energy consumption, and makespan are often the main scheduling objectives. In this work, we investigate dynamic scheduling of tasks modelled by directed acyclic graphs (DAGs), which is an NP-hard problem with all existing methods being heuristics. Our contributions have two steps: (i) Assuming that the allocation of DAG nodes to processors is given, we propose OEA (Optimal Energy Allocation) and SOEA (Search-based OEA)-the first optimal methods that minimise the energy consumption whilst satisfying the reliability requirement-for homogeneous and heterogeneous systems, respectively; (ii) We present a novel scheduling algorithm ODS (Out-Degree Scheduling) that allocates the DAG nodes according to their out-degrees, and considering energy consumption, reliability, as well as dynamic finish time. ODS dominates the widely applied HEFT (Heterogeneous Earliest Finish Time) in makespan. Combining SOEA with ODS makes a complete solution to the problem of dynamic DAG scheduling on multiprocessor systems, and achieves generally better results compared to the existing approaches. Specifically, in most cases, we are better on all the three objectives, i.e., reliability, energy as well as makespan, and in other cases we are better on some of the objectives.

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Section: B Experimental Platform and Dag Applicationsmentioning

confidence: 99%

“…Another important objective is to minimise the energy consumption. Taking the autonomous vehicles as an example, the power consumption of the computing system is similar to that of traction [1], [2]. The energy storage of battery-powered devices, such as electric vehicles and drones, is especially limited.…”

Section: Introductionmentioning

confidence: 99%

Dynamic DAG Scheduling on Multiprocessor Systems: Reliability, Energy, and Makespan

Huang

Jiao

et al. 2020

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

View full text Add to dashboard Cite

show abstract

“…• Only worst-case workload information is known: This situation is quite common for a control designer. Worstcase response time or delay of the algorithms can be analysed (where often times are pessimistic) through profiling and/or analytical methods [4]. The control designer is then given only the worst-case timing information and is asked to design a controller with a QoC requirement.…”

Section: Design Guidelinesmentioning

confidence: 99%

“…The combination of the camera and image processing (sensing) gives necessary information on parameters such as relative position, geometry, relative distance, depth perception and tracking of the objectof-interest. This enables the effective use of low-cost camera sensors to enable new functionality or replace expensive sensors in cost-sensitive industries like automotive [1][3] [4].…”

Section: Introductionmentioning

confidence: 99%