The development of Integrated Circuits for the Automotive sector imposes on major challenges. ISO26262 compliance, as part of this process, entails complex analysis for the evaluation of potential random hardware faults. This paper proposes a systematic approach to identify faults that do not disrupt safety-critical functionalities and consequently can be considered Safe. By deploying code coverage and Formal verification techniques, our methodology enables the classification of faults that are unclassified by other technologies, improving ISO26262 compliance. Our results, in combination with Fault Simulation, achieved a Diagnostic Coverage of 93% in a CAN Controller. These figures allow an initial assessment for an ASIL B configuration of the IP.

The current demands for autonomous driving generated momentum for an increase in research in the different technologies required for these applications. Nonetheless, the limited access to representative designs and industrial methodologies poses a challenge to the research community. Considering this scenario, there is a high demand for an open-source solution that could support development of research targeting automotive applications. This paper presents the current status of AutoSoC, an automotive SoC benchmark suite that includes hardware and software elements and is entirely open-source. The objective is to provide researchers with an industrial-grade automotive SoC that includes all essential components, is fully customizable, and enables analysis of functional safety solutions and automotive SoC configurations. This paper describes the available configurations of the benchmark including an initial assessment for ASIL B to D configurations.

1-Nowadays, General Purpose Graphics Processing Units (GPGPUs) devices are considered as promising solutions for high-performance safety-critical applications, such as those in the automotive field. However, their adoption requires solutions to effectively detect faults arising in the device during the operative life. Hence, effective in-field test solutions are required to guarantee high-reliability levels. In this paper, we leverage the results of Software-Based Self-Test (SBST) based approaches for GPGPUs by deploying new techniques for automating the identification of untestable faults (UF). Our methodology has achieved fault coverage of 82.8% when applied to an open-source implementation of the NVIDIA G80 GPU architecture. The proposed approach combining SBSTs and UFs identification appears as an effective solution for the reliability analysis of GPGPUs.

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