MutateNN: Mutation Testing of Image Recognition Models Deployed on Hardware Accelerators

The increased utilization of Artificial Intelligence (AI) solutions brings with it inherent risks, such as misclassification and sub-optimal execution time performance, due to errors introduced in their deployment infrastructure because of problematic configuration and software faults. On top of that, AI methods such as Deep Neural Networks (DNNs) are utilized to perform demanding, resource-intensive and even safety-critical tasks, and in order to effectively increase the performance of the DNN models deployed, a variety of Machine Learning (ML) compilers have been …

accelerators artificial artificial intelligence cs.lg cs.se cs.sy deployment eess.sy errors hardware image image recognition infrastructure intelligence mutation networks neural networks performance recognition risks software solutions testing