Nowadays, Deep Neural Networks (DNNs) are employed in many fields to perform different tasks, such as autonomous driving, always listening, augmented reality, computer vision and many others. Some of the listed tasks, as the autonomous driving, are safety-critical. Aware of that, ensuring the reliability of this technology is very important since, an error of assessment could endanger human lives. DNNs are known to be intrinsically error resilient since, their topology exploits data redundancy. Therefore, even if an error occurs the DNNs might be still capable to correctly predict the result. However, this may change whenever the DNNs are implemented in Hardware (HW) for the reason that, the target HW have limited processing resources in which multiple neurons are mapped and potentially be corruptible by a single fault.