Deep Neural Networks (DNNs) are being increasingly used in safety-critical applications, from healthcare to autonomous driving. Furthermore, as new frontiers unfold, such as the growing New Space Economy, DNNs are expected to be widely deployed in outer space and other hazardous environments soon. However, their prediction accuracy was shown to degrade in presence of transient hardware faults, leading to unpredictable and potentially catastrophic errors. As these kinds of problems are frequent in radiation-prone domains such as space, it is of the utmost importance to strenghten the DNNs' resistance to computational or parameters errors. In the relevant literature, multiple fault tolerance techniques have been researched, which limit the consequences of potential faults.