As machine learning (ML) has seen increasing adoption in safety-critical domains (e.g., autonomous vehicles), the reliability of ML systems has also grown in importance. While prior studies have proposed techniques to enable efficient error-resilience (e.g., selective instruction duplication), a fundamental requirement for realizing these techniques is a detailed understanding of the application's resilience. In this work, we present TensorFI 1 and TensorFI 2, high-level fault injection (FI) frameworks for TensorFlow-based applications. TensorFI 1 and 2 are able to inject both hardware and software faults in any general TensorFlow 1 and 2 program respectively. Both are configurable FI tools that are flexible, easy to use, and portable. They can be integrated into existing TensorFlow programs to assess their resilience for different fault types (e.g., bit-flips in particular operations or layers). We use TensorFI 1 and TensorFI 2 to evaluate the resilience of 11 and 10 ML programs respectively, all written in TensorFlow, including DNNs used in the autonomous vehicle domain. The results give us insights into why some of the models are more resilient. We also measure the performance overheads of the two injectors, and present 4 case studies, two for each tool, to demonstrate their utility.

Machine learning is becoming more prevalent in safety-critical systems like autonomous vehicles and medical imaging. Faulty training data, where data is either misla-belled, missing, or duplicated, can increase the chance of misclassification, resulting in serious consequences. In this paper, we evaluate the resilience of ML ensembles against faulty training data, in order to understand how to build better ensembles. To support our evaluation, we develop a fault injection framework to systematically mutate training data, and introduce two diversity metrics that capture the distribution and entropy of predicted labels. Our experiments find that ensemble learning is more resilient than any individual model and that high accuracy neural networks are not necessarily more resilient to faulty training data. Further, we find that simple majority voting suffices in most cases for resilience in ML ensembles. Finally, we observe diminishing returns for resilience as we increase the number of models in an ensemble. These findings can help machine learning developers build ensembles that are both more resilient and more efficient.

As machine learning (ML) has seen increasing adoption in safety-critical domains (e.g., autonomous vehicles), the reliability of ML systems has also grown in importance. While prior studies have proposed techniques to enable efficient error-resilience (e.g., selective instruction duplication), a fundamental requirement for realizing these techniques is a detailed understanding of the application's resilience. In this work, we present TensorFI, a high-level fault injection (FI) framework for TensorFlow-based applications. TensorFI is able to inject both hardware and software faults in general TensorFlow programs. TensorFI is a configurable FI tool that is flexible, easy to use, and portable. It can be integrated into existing TensorFlow programs to assess their resilience for different fault types (e.g., faults in particular operators). We use TensorFI to evaluate the resilience of 12 ML programs, including DNNs used in the autonomous vehicle domain. The results give us insights into why some of the models are more resilient. We also present two case studies to demonstrate the usefulness of the tool. TensorFI is publicly available at https://github.com/DependableSystemsLab/TensorFI.