A Framework for Developing Robust Machine Learning Models in Harsh Environments: A Review of CNN Design Choices

Machine Learning algorithms are envisioned to be used in harsh and/or safety critical environments such as self-driving cars, aerospace, and nuclear sites where the effects of radiation can cause errors in electronics known as Single Event Effects (SEEs). The effect of SEEs on machine learning models, such as neural networks composed of millions of parameters, is currently unknown. Understanding the models in terms of robustness and reliability is essential for their use in these environments. To facilitate this understanding, we propose a novel framework to simulate SEEs during model training and inference. Using the framework we investigate the robustness of the Convolutional Neural Network (CNN) architecture with dropout, regularisa-tion and activation functions under different error models. Two new activation functions are suggested that decrease error by up to 40% compared to ReLU. We also investigate an alternative pooling layer that can provide model robustness with a 16% decr ease in error with ReLU. Overall, our results confirm the efficacy of the framework for evaluating model robustness in harsh environments.