Assessing Robustness of Image Recognition Models to Changes in the Computational Environment

Image recognition tasks typically use deep learning and require enormous processing power, thus relying on hardware accelerators like GPUs and TPUs for fast, timely processing. Failure in real-time image recognition tasks can occur due to incorrect mapping on hardware accelerators, which may lead to timing uncertainty and incorrect behavior. In addition, the increasing demand for optimal performance has led to progress towards the optimization of different neural network operations, such as operator fusion.Owing to the increased use of image recognition tasks in safety-critical applications like autonomous driving and medical imaging, it is imperative to assess the performance and impact of such optimizations, and explore their effectiveness. In this paper we conduct robustness analysis of four popular image recognition models with the ImageNet dataset, assessing the impact of the compiler optimizations applied, utilizing different Deep Learning frameworks and executing on hardware devices of varying capabilities. We report the impact in terms of misclassifications and inference time across varying settings.