In recent years, Deep Neural Networks (DNNs) have become increasingly present and used in any field, and they are now a fundamental element for most artificial intelligence applications. Today, these networks are being introduced in the medical, automotive, financial and industrial fields (among the others), which today require high safety parameters. Hardware faults can compromise the functionality of the DDNs and the safety of the system on which they are connected; the increase use of DNNs in safety-critical domains has led to the need to accompany their exceptional performance with diagnostic mechanisms to ensure safety and reliability. Therefore, the issue of network resilience has become very important and it is now mandatory to introduce the ability to maintain standards performance while managing to inhibit, or correct, factors such as hardware-induced faults, wrong inputs and any type of disturbance.