Evaluating SEU Resilience of CNNs with Fault Injection

Abstract

Convolutional neural networks (CNNs) are quickly growing as a solution for advanced image processing in many mission-critical high-performance and embedded computing systems ranging from supercomputers and data centers to aircraft and spacecraft. However, the systems running CNNs are increasingly susceptible to single-event upsets (SEUs) which are bit flips that result from charged particle strikes. To better understand how to mitigate the effects of SEUs on CNNs, the behavior of CNNs when exposed to SEUs must be better understood. Software fault-injection tools allow us to emulate SEUs to analyze the effects of various CNN architectures and input data features on overall resilience. Fault injection on three combinations of CNNs and datasets yielded insights into their behavior. When focusing on a threshold of 1% error in classification accuracy, more complex CNNs tended to be less resilient to SEUs, and easier classification tasks on well-clustered input data were more resilient to SEUs. Overall, the number of bits flipped to reach this threshold ranged from 20 to 3,790 bits. Results demonstrate that CNNs are highly resilient to SEUs, but the complexity of the CNN and difficulty of the classification task will decrease that resilience.