Emulating SDC-1 Hardware Faults Through Application Level Bit-Flip Injections in QNNs

Nowadays, quantized Neural Networks (QNNs) are increasingly deployed in safety-critical scenarios based on resource-constrained edge devices, making resilience evaluation of paramount importance. Current studies investigate transient or permanent faults in a separate way leaving a gap between hardware- and application-level fault analysis. This paper deals with this gap by presenting a novel fault-correlation methodology that analyses the QNN behavior under transient bit-flips and permanent stuck-at faults. The proposed approach experimentally demonstrates that the impact of permanent hardware faults, which are known to be computationally costly, can be effectively estimated through extremely fast application-level fault injections. The experimental results have been gathered in three image-classification QNNs (MobileNetV1, EfficientNetB0, and ResNet18) deployed in a RISC-V based SoC, and show an average cross-correlation of 87.05% in the three experiments between the two fault models under evaluation.