Re-Thinking Mixed-Criticality Architecture for Automotive Industry

Jiang, Zhe; Zhao, Shuai; Dong, Pan; Yang, Dawei; Wei, Ran; Guan, Nan; Audsley, Neil

doi:10.1109/iccd50377.2020.00092

Cited by 11 publications

(4 citation statements)

References 14 publications

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“…Jiang et al highlighted the gap between mixed-criticality theoretical models and industrial standards [15], the cause of which is that implementing theoretical models in industry is difficult due to the absence of industrial safety standards considerations during the development of these models. The authors narrowed this gap by extending the widely used mixed-criticality model, adaptive mixed criticality (AMC) [16], into a generic industrial architecture (Z-MC) that considers industrial safety standards.…”

Section: Related Workmentioning

confidence: 99%

ATMP-CA: Optimising Mixed-Criticality Systems Considering Criticality Arithmetic

2021

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Many safety-critical systems use criticality arithmetic, an informal practice of implementing a higher-criticality function by combining several lower-criticality redundant components or tasks. This lowers the cost of development, but existing mixed-criticality schedulers may act incorrectly as they lack the knowledge that the lower-criticality tasks are operating together to implement a single higher-criticality function. In this paper, we propose a solution to this problem by presenting a mixed-criticality mid-term scheduler that considers where criticality arithmetic is used in the system. As this scheduler, which we term ATMP-CA, is a mid-term scheduler, it changes the configuration of the system when needed based on the recent history of deadline misses. We present the results from a series of experiments that show that ATMP-CA’s operation provides a smoother degradation of service compared with reference schedulers that do not consider the use of criticality arithmetic.

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Section: Related Workmentioning

confidence: 99%

ATMP-CA: Optimising Mixed-Criticality Systems Considering Criticality Arithmetic

2021

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“…Deep neural networks (DNNs) have been widely used for solving complex problems across a wide range of domains, including computer vision, speech processing, and robotics [1][2][3][4], while DNNs can achieve remarkable results on high-performance cloudservers, it is still expected to perform efficiently when used locally on mobile/embedded devices, due to connectivity and latency limitations, as well as privacy and security concerns [5,6]. Since mobile devices have tight latency, throughput, and energy constraints, many specialized DNN-inference accelerators, which achieve compelling results compared to traditional CPUs and GPUs, have been proposed [7].…”

Section: Introductionmentioning

confidence: 99%

Nebula: A Scalable and Flexible Accelerator for DNN Multi-Branch Blocks on Embedded Systems

Yang

et al. 2022

Electronics

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Deep neural networks (DNNs) are widely used in many artificial intelligence applications; many specialized DNN-inference accelerators have been proposed. However, existing DNN accelerators rely heavily on certain types of DNN operations (such as Conv, FC, and ReLU, etc.), which are either less used or likely to become out of date in future, posing challenges of flexibility and compatibility to existing work. This paper designs a flexible DNN accelerator from a more generic perspective rather than speeding up certain types of DNN operations. Our proposed Nebula exploits the width property of DNNs and gains a significant improvement in system throughput and energy efficiency over multi-branch architectures. Nebula is a first-of-its-kind framework for multi-branch DNNs.

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“…Partitions are strictly independent execution environments protected from each other, and they are used when application components have different criticality levels; it is crucial to avoid that low criticality components jeopardize the execution of high criticality ones. Recent works [1] [2] remark the interest on virtualization/partitioning techniques at many industrial domains, such as automotive or railway, as key issues to be addressed when developing safety-critical applications, and in [3] the will of train manufacturers to re-factor their applications designs is shown, in order to allow the execution of applications with different criticality levels.…”

Section: Introductionmentioning

confidence: 99%

How windows size and number can influence the schedulability of hierarchically-scheduled time-partitioned distributed real-time systems

et al. 2022

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Partitioning techniques are implemented in the development of safety-critical applications to ensure isolation among components. An adequate scheduling of the execution of such partitions is a key challenge so that applications meet the hard deadlines imposed. In this work, we study the effect of different partition window configuration parameters, with the aim of analyzing their impact in the worst-case response times of system tasks. This is the first step in the development of an algorithm for optimizing partition windows in hierarchically-scheduled time partitioned distributed systems.

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Re-Thinking Mixed-Criticality Architecture for Automotive Industry

Cited by 11 publications

References 14 publications

ATMP-CA: Optimising Mixed-Criticality Systems Considering Criticality Arithmetic

ATMP-CA: Optimising Mixed-Criticality Systems Considering Criticality Arithmetic

Nebula: A Scalable and Flexible Accelerator for DNN Multi-Branch Blocks on Embedded Systems

How windows size and number can influence the schedulability of hierarchically-scheduled time-partitioned distributed real-time systems

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