Pub Date : 2022-10-01DOI: 10.1109/EMSOFT55006.2022.00017
Kai-Björn Gemlau, H. V. Hasseln, R. Ernst
The way how automotive software is developed has rapidly evolved with the introduction of heterogeneous hardware/software architectures. Nevertheless, the requirement for deterministic behavior of safety-critical cause-effect chains persists unchanged. As a side effect of the shared platform, complex dependencies between critical and non-critical functions arise, demanding a model-based approach to handle time determinism throughout the design process. Limited to the scope of a single component, the Logical Execution Time (LET) paradigm provides such an abstraction of the runtime behavior. It has been successfully introduced in AUTOSAR to mitigate the design complexity, ensure a deterministic timing behavior and facilitate a lock-free communication. This paper discusses how the scope of LET can be extended to the system level, enabling an efficient design of distributed AUTOSAR software, where robustness towards platform changes plays a key role. System-Level Logical Execution Time (SL-LET) is currently in the process of AUTOSAR standardization, supported by a joint group of industry and academic partners.
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Pub Date : 2022-10-01DOI: 10.1109/EMSOFT55006.2022.00016
Michael Abraham, Aaron Mayne, Tristan Perez, Ítalo Romani de Oliveira, Huafeng Yu, Chiao Hsieh, Yangge Li, Dawei Sun, S. Mitra
Deep learning (DL) models are becoming effective in solving computer-vision tasks such as semantic segmentation, object tracking, and pose estimation on real-world captured images. Reliability analysis of autonomous systems that use these DL models as part of their perception systems have to account for the performance of these models. Autonomous systems with traditional sensors have tried-and-tested reliability assessment processes with modular design, unit tests, system integration, compositional verification, certification, etc. In contrast, DL perception modules relies on data-driven or learned models. These models do not capture uncertainty and often lack robustness. Also, these models are often updated throughout the lifecycle of the product when new data sets become available. However, the integration of an updated DL-based perception requires a reboot and start afresh of the reliability assessment and operation processes for autonomous systems. In this paper, we discuss three challenges related to specifying, verifying, and operating systems that incorporate DL-based perception. We illustrate these challenges through two concrete and open source examples.
{"title":"Industry-track: Challenges in Rebooting Autonomy with Deep Learned Perception","authors":"Michael Abraham, Aaron Mayne, Tristan Perez, Ítalo Romani de Oliveira, Huafeng Yu, Chiao Hsieh, Yangge Li, Dawei Sun, S. Mitra","doi":"10.1109/EMSOFT55006.2022.00016","DOIUrl":"https://doi.org/10.1109/EMSOFT55006.2022.00016","url":null,"abstract":"Deep learning (DL) models are becoming effective in solving computer-vision tasks such as semantic segmentation, object tracking, and pose estimation on real-world captured images. Reliability analysis of autonomous systems that use these DL models as part of their perception systems have to account for the performance of these models. Autonomous systems with traditional sensors have tried-and-tested reliability assessment processes with modular design, unit tests, system integration, compositional verification, certification, etc. In contrast, DL perception modules relies on data-driven or learned models. These models do not capture uncertainty and often lack robustness. Also, these models are often updated throughout the lifecycle of the product when new data sets become available. However, the integration of an updated DL-based perception requires a reboot and start afresh of the reliability assessment and operation processes for autonomous systems. In this paper, we discuss three challenges related to specifying, verifying, and operating systems that incorporate DL-based perception. We illustrate these challenges through two concrete and open source examples.","PeriodicalId":371537,"journal":{"name":"2022 International Conference on Embedded Software (EMSOFT)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128930039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-01DOI: 10.1109/EMSOFT55006.2022.00008
Juneseo Chang, Sejong Oh, Daejin Park
Detecting transient faults in safety-critical neural network (NN) applications operated on embedded systems has become a concern, but it is challenging to achieve high accuracy because of the open context problem and resource constraints. This study proposes an accuracy-area efficient, data-analysis-based online soft errors (SEs) and control flow errors (CFEs) detection, applicable to any NN application with low overhead. We insert code for runtime monitoring data assertion, and the data are distributed to shallow or deep detection models selectively. The shallow detection model detects CFEs by verifying runtime signatures with values obtained from simulations, and detects SEs of data having constant values according to program input. SEs of other data are verified by a deep detection model using a sliding window one-class support vector machine. Fault injection experiments on an image classification NN showed that our detector has significant detection accuracy in fault conditions.
{"title":"Work-in-Progress: Accuracy-Area Efficient Online Fault Detection for Robust Neural Network Software-Embedded Microcontrollers","authors":"Juneseo Chang, Sejong Oh, Daejin Park","doi":"10.1109/EMSOFT55006.2022.00008","DOIUrl":"https://doi.org/10.1109/EMSOFT55006.2022.00008","url":null,"abstract":"Detecting transient faults in safety-critical neural network (NN) applications operated on embedded systems has become a concern, but it is challenging to achieve high accuracy because of the open context problem and resource constraints. This study proposes an accuracy-area efficient, data-analysis-based online soft errors (SEs) and control flow errors (CFEs) detection, applicable to any NN application with low overhead. We insert code for runtime monitoring data assertion, and the data are distributed to shallow or deep detection models selectively. The shallow detection model detects CFEs by verifying runtime signatures with values obtained from simulations, and detects SEs of data having constant values according to program input. SEs of other data are verified by a deep detection model using a sliding window one-class support vector machine. Fault injection experiments on an image classification NN showed that our detector has significant detection accuracy in fault conditions.","PeriodicalId":371537,"journal":{"name":"2022 International Conference on Embedded Software (EMSOFT)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131258754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-06DOI: 10.1109/EMSOFT55006.2022.00018
Shaoshan Liu, Xiaoming Li, Tongsheng Geng, Stéphane Zuckerman, J. Gaudiot
One key technical challenge in the age of autonomous machines is the programming of autonomous machines, which demands the synergy across multiple domains, including fundamental computer science, computer architecture, and robotics, and requires expertise from both academia and industry. This paper discusses the programming theory and practices tied to producing real-life autonomous machines, and covers aspects from high-level concepts down to low-level code generation in the context of specific functional requirements, performance expectation, and implementation constraints of autonomous machines.
{"title":"Programming Autonomous Machines : Special Session Paper","authors":"Shaoshan Liu, Xiaoming Li, Tongsheng Geng, Stéphane Zuckerman, J. Gaudiot","doi":"10.1109/EMSOFT55006.2022.00018","DOIUrl":"https://doi.org/10.1109/EMSOFT55006.2022.00018","url":null,"abstract":"One key technical challenge in the age of autonomous machines is the programming of autonomous machines, which demands the synergy across multiple domains, including fundamental computer science, computer architecture, and robotics, and requires expertise from both academia and industry. This paper discusses the programming theory and practices tied to producing real-life autonomous machines, and covers aspects from high-level concepts down to low-level code generation in the context of specific functional requirements, performance expectation, and implementation constraints of autonomous machines.","PeriodicalId":371537,"journal":{"name":"2022 International Conference on Embedded Software (EMSOFT)","volume":"876 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134241762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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