TY - GEN
T1 - Fast and Accurate Model-Driven FPGA-based System-Level Fault Emulation
AU - Kaja, Endri
AU - Gerlin, Nicolas
AU - Bora, Monideep
AU - Rutsch, Gabriel
AU - Devarajegowda, Keerthikumara
AU - Stoffel, Dominik
AU - Kunz, Wolfgang
AU - Ecker, Wolfgang
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Safety-critical designs need to ensure reliable operations even under a hostile working environment with a certain degree of confidence. Continuous technology scaling has resulted in designs being more susceptible to the risk of failure. As a result, the safety requirements are constantly evolving and becoming more stringent. For validating and measuring the robustness of safety-critical designs, fault injection methods are employed within the design flows. To ensure safety requirements' compliance, and at the same time to cope with the ever-increasing complexity of modern SoCs, the existing design flows become inadequate as the process is repetitive, time-tedious, and requires high manual efforts. In this paper, a fully automated, fast and accurate, fault emulation framework based on the FPGA platform is proposed that enables a high level of controllability and observability for fault injection. The approach uses model-driven engineering concepts and automates various fault injection campaigns, namely, statistical fault injection (SFI), direct fault injection (DFI), and exhaustive fault injection (EFI). A novel design architecture tailored for the FPGA platform is also proposed to improve the overall productivity of performing fault emulation. The proposed approach scales to a wide variety of RISC-V based CPU subsystems with varying complexity in size and features. The experimental results demonstrate a significant gain in the fault emulation performance by a factor of 2.75x to 47.57x when compared to the standard simulation-based fault injection methods.
AB - Safety-critical designs need to ensure reliable operations even under a hostile working environment with a certain degree of confidence. Continuous technology scaling has resulted in designs being more susceptible to the risk of failure. As a result, the safety requirements are constantly evolving and becoming more stringent. For validating and measuring the robustness of safety-critical designs, fault injection methods are employed within the design flows. To ensure safety requirements' compliance, and at the same time to cope with the ever-increasing complexity of modern SoCs, the existing design flows become inadequate as the process is repetitive, time-tedious, and requires high manual efforts. In this paper, a fully automated, fast and accurate, fault emulation framework based on the FPGA platform is proposed that enables a high level of controllability and observability for fault injection. The approach uses model-driven engineering concepts and automates various fault injection campaigns, namely, statistical fault injection (SFI), direct fault injection (DFI), and exhaustive fault injection (EFI). A novel design architecture tailored for the FPGA platform is also proposed to improve the overall productivity of performing fault emulation. The proposed approach scales to a wide variety of RISC-V based CPU subsystems with varying complexity in size and features. The experimental results demonstrate a significant gain in the fault emulation performance by a factor of 2.75x to 47.57x when compared to the standard simulation-based fault injection methods.
KW - Emulation Architecture
KW - Fault Emulation
KW - Mixed granularity design
KW - Model-driven generation
KW - Safety analysis
UR - http://www.scopus.com/inward/record.url?scp=85142454442&partnerID=8YFLogxK
U2 - 10.1109/VLSI-SoC54400.2022.9939615
DO - 10.1109/VLSI-SoC54400.2022.9939615
M3 - Conference contribution
AN - SCOPUS:85142454442
T3 - IEEE/IFIP International Conference on VLSI and System-on-Chip, VLSI-SoC
BT - Proceedings of the 2022 IFIP/IEEE 30th International Conference on Very Large Scale Integration, VLSI-SoC 2022
PB - IEEE Computer Society
T2 - 30th IFIP/IEEE International Conference on Very Large Scale Integration, VLSI-SoC 2022
Y2 - 3 October 2022 through 5 October 2022
ER -