Deep Neural Networks (DNNs) are currently one of the most intensively and widely used predictive models in the field of machine learning. DNNs have proven to give very good results for many complex tasks and applications, such as object recognition in images/videos, natural language processing, satellite image recognition, robotics, aerospace, smart healthcare, and autonomous driving. Nowadays, there is intense activity in designing custom Artificial Intelligence (AI) hardware accelerators to support the energy-hungry data movement, speed of computation, and memory resources that DNNs require to realize their full potential. Hardware for AI (HW-AI), similar to traditional computing hardware, is subject to hardware faults (HW faults) that can have several sources: variations in fabrication process parameters, fabrication process defects, latent defects, i.e., defects undetectable at time-zero post-fabrication testing that manifest themselves later in the field of application, silicon ageing, e.g., time-dependent dielectric breakdown, or even environmental stress, such as heat, humidity, vibration, and Single Event Upsets (SEUs) stemming from ionization.