Reliability of Deep Neural Networks: Impact and Open Issues

Nowadays, Deep Neural Networks (DNNs) are widely used in safety-critical fields such as automotive and healthcare, where their reliability is crucial due to their direct impact on human lives. Over the years, evaluating their resilience through software-level fault injection experiments has become a common research approach. The corruption of individual bits in the model’s parameters has been one of the most studied fault models in the last decade. This work introduces a methodology to evaluate the impact of permanent faults on DNN weights in image classification and object detection tasks, highlighting key ideas, main contributions, and the research’s impact over time.