Efficient Fault Injection based on Dynamic HDL Slicing Technique

This work proposes a fault injection methodology where Hardware Description Language (HDL) code slicing is exploited to prune fault injection locations, thus enabling more efficient campaigns for safety mechanisms evaluation. In particular, the dynamic HDL slicing technique provides for a highly collapsed critical fault list and allows avoiding injections at redundant locations or time-steps. Experimental results show that the proposed methodology integrated into commercial tool flow doubles the simulation speed when comparing to the state-of-the-art industrial-grade EDA tool flows.Comment: arXiv admin note: substantial text overlap with arXiv:2001.0998

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

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

Combining Fault Analysis Technologies for ISO26262 Functional Safety Verification

The development of Integrated Circuits for the Automotive sector imposes on complex challenges. ISO26262 Functional Safety requirements entail extensive Fault Injection campaigns and complex analysis for the evaluation of deployed Software Tools. This paper proposes a methodology to improve Fault Analysis Tools Confidence Level (TCL) by detecting errors in the classification of faults. By combining the strengths of Automatic Test Pattern Generators (ATPG), Formal Methods and Fault Injection Simulators we are able to automatically generate a Test Environment that enables the validation of the tools and provides supplementary information about the design behavior. Our results showed fault detection rates above 99% including information to improve ISO26262 metrics calculationAccepted author manuscriptComputer EngineeringQuantum & Computer Engineerin

Efficient Methodology for ISO26262 Functional Safety Verification

Tolerance to random hardware failures, required by ISO26262, entails accurate design behavior analysis, complex Verification Environments and expensive Fault Injection campaigns. This paper proposes a methodology combining the strengths of Automatic Test Pattern Generators (ATPG), Formal Methods and Fault Injection Simulation to decrease the efforts of Functional Safety Verification. Our methodology results in a fast-deployed Fault Injection environment achieving Fault detection rates higher than 99% on the tested designs. In addition, ISO26262 Tool Confidence level is improved by a fault analysis report that allows verification of malfunctions in the outputs of the tools.Accepted author manuscriptComputer EngineeringQuantum & Computer Engineerin

Use of Formal Methods for verification and optimization of Fault Lists in the scope of ISO26262

This work aims at an alternative method to verify the correctness of Fault Lists generated by fault simulators tools in context of safety verification. The lists generated by simulation tools are verified against lists from formal tools. The consistency evaluation between the lists supports the Tool Confidence Level (TCL) assessment, defined in the ISO26262. In addition, formal tools have the potential of performing optimization in Fault Lists by annotation of the expected behavior of the design under fault. Our work demonstrates the feasibility of using Formal Methods to verify and optimize the fault list from simulators. Results indicate an average reduction of 29.5% on the number of faults to be simulated and demonstrate that it is possible to achieve TCL by verification of the fault lists.Secure hardwareQuantum & Computer Engineerin

Efficient Methodology for ISO26262 Functional Safety Verification

Tolerance to random hardware failures, required by ISO26262, entails accurate design behavior analysis, complex Verification Environments and expensive Fault Injection campaigns. This paper proposes a methodology combining the strengths of Automatic Test Pattern Generators (ATPG), Formal Methods and Fault Injection Simulation to decrease the efforts of Functional Safety Verification. Our methodology results in a fast-deployed Fault Injection environment achieving Fault detection rates higher than 99% on the tested designs. In addition, ISO26262 Tool Confidence level is improved by a fault analysis report that allows verification of malfunctions in the outputs of the tools.</p

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

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.Computer EngineeringQuantum & Computer Engineerin

Using STLs for Effective In-field Test of GPUs

Reliability and functional safety are of primary importance for Graphics Processing Units (GPUs) widely used in safety-critical fields such as automotive and robotics. Moreover, cutting-edge technologies used in modern GPUs make these even more susceptible to in-field permanent faults due to aging and degradation; hence, the rising need for effective in-field test solutions to ensure the deployment of appropriate countermeasures. This work demonstrates that Self-Test Libraries (STLs) can be written and effectively applied for the in-field detection of permanent faults in a GPU. Simulations using an open-source GPU model (FlexGripPlus) confirm the validity and effectiveness of STLs. The results show that a fault coverage of 92.6% can be achieved, allowing at least ASIL B level for functional safety inside a GPU core