Efficient Multi-core AUTOSAR-Platform Based on an Input/Output Gateway Core

Urbina, Moises; Obermaisser, Roman

doi:10.1109/pdp.2017.85

Cited by 4 publications

(1 citation statement)

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“…However, the AUTOSAR multicore extensions were only designed for a simple hardware model in which cores access the main memory and other peripherals over a shared bus [4]. To meet the requirements of future automotive applications, there is great interest in extending the existing multicore capabilities and adding support for additional hardware architectures like clustered manycores [4,12,22].…”

Section: Introductionmentioning

confidence: 99%

Redundant dataflow applications on clustered manycore architectures

Kuhbacher

Ungerer

Altmeyer

2022

Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing

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Increasing performance requirements in the embedded systems domain have encouraged a drift from singlecore to multicore processors. Cars are an example for complex embedded systems in which the use of multicores continues to grow. The requirements of software components running in modern cars are diverse. On the one hand there are safety-critical tasks like the airbag control, on the other hand tasks which do not have any safety-related requirements at all, for example those controlling the infotainment system. Trends like autonomous driving lead to tasks which are simultaneously safety-critical and computationally complex. To satisfy the requirements of modern embedded applications we developed a dataflow-based runtime environment (RTE) for clustered manycore architectures. The RTE is able to execute dataflow graphs in various redundancy configurations and with different schedulers. We implemented our RTE design on the Kalray Bostan Massively Parallel Processor Array and evaluated all possible configurations for three common computation tasks. To classify the performance of our RTE, we compared the non-redundant graph executions with OpenCL versions of the three applications. The results show that our RTE can come close or even surpass Kalray's OpenCL framework, although maximum performance was not the primary goal of our design.

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Section: Introductionmentioning

confidence: 99%