NPU-Aware Fault Injection and Statistical Sensitivity Analysis for CNN Reliability Evaluation

Artificial intelligence (AI) is propelling space exploration into a new era. Synergistic breakthroughs in chip design and high-speed communications have facilitated the large-scale deployment of on-board satellite computing. Assessing the reliability of these systems via fault injection (FI) remains difficult due to the massive computational demands of Convolutional Neural Networks (CNNs) and the complex architectures of Neural Processing Units (NPUs). This research presents a high-precision, efficient FI methodology specifically tailored for NPU architectures to optimize both evaluation accuracy and execution efficiency. Implementing a hierarchical injection strategy to identify fault-sensitive layers minimizes computational overhead while ensuring statistical validity. Experimental results on the ResNet-50 network demonstrate that the proposed methodology constrains accuracy degradation to less than 0.1% while achieving a 60.80% reduction in total execution time.