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Automated Identification of Application-Dependent Safe Faults in Automotive Systems-on-a-Chips

Journal Article (2022)
Author(s)

Ahmet Cagri Bagbaba (Tallinn University of Technology, Cadence Design Systems)

Felipe Silva (Cadence Design Systems, TU Delft - Computer Engineering)

Matteo Sonza Reorda (Politecnico di Torino)

S Hamdioui (TU Delft - Quantum & Computer Engineering)

Maksim Jenihhin (Tallinn University of Technology)

Christian Sauer (Cadence Design Systems)

Research Group
Computer Engineering
Copyright
© 2022 Ahmet Cagri Bagbaba, F. Augusto da Silva, Matteo Sonza Reorda, S. Hamdioui, Maksim Jenihhin, Christian Sauer
DOI related publication
https://doi.org/10.3390/electronics11030319
More Info
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Publication Year
2022
Language
English
Copyright
© 2022 Ahmet Cagri Bagbaba, F. Augusto da Silva, Matteo Sonza Reorda, S. Hamdioui, Maksim Jenihhin, Christian Sauer
Research Group
Computer Engineering
Issue number
3
Volume number
11
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Abstract

ISO 26262 requires classifying random hardware faults based on their effects (safe, detected, or undetected) within integrated circuits used in automobiles. In general, this classification is addressed using expert judgment and a combination of tools. However, the growth of integrated circuit complexity creates a huge fault space; hence, this form of fault classification is error prone and time consuming. Therefore, an automated and systematic approach is needed to target hardware fault classification in automotive systems on chips (SoCs), considering the application software. This work focuses on identifying safe faults: the proposed approach utilizes coverage analysis to identify candidate safe faults considering all the constraints coming from the application. Then, the behavior of the application software is modeled so that we can resort to a formal analysis tool. The proposed technique is evaluated on the AutoSoC benchmark running a cruise control application. Resorting to our approach, we could classify 20%, 11%, and 13% of all faults in the central processing unit (CPU), universal asynchronous receiver–transmitter (UART), and controller area network (CAN) as safe faults, respectively. We also show that this classification can increase the diagnostic coverage of software test libraries targeting the CPU and CAN modules by 4% to 6%, increasing the achieved testable fault coverage.