DEAR-CNN: Data-Efficient Assessment of Resiliency in Convolutional Neural Networks

Convolutional Neural Networks (CNNs) are widely employed in various domains, including safety-critical applications such as autonomous driving. In these scenarios, the reliability of CNNs can be compromised by hardware faults occurring during inference, potentially leading to severe consequences. Evaluating the resilience of CNNs to hardware faults is primarily conducted through Fault Injection (FI) campaigns. However, a significant challenge lies in selecting an appropriate workload. Typically, the entire test set is applied for every injected fault, making the process highly time-consuming and posing difficulties for timely assessments. This paper investigates image selection strategies to rank inputs from the test dataset based on their difficulty in being classified by the CNN. The objective is to identify a minimal subset of test data that enables reliable CNN assessment while reducing computational overhead. By prioritizing challenging samples, the proposed method focuses on inputs that are more likely to reveal network vulnerabilities under fault conditions, enhancing the efficiency of the reliability evaluation process. Experimental results demonstrate that using such a subset of the test data suffices to estimate the number of critical faults for at least one image. This approach not only accelerates the reliability evaluation but also provides novel insights into CNN reliability, offering a practical framework for continuous assessments.