Emergence of Deep Neural Networks (DNN) has led to a proliferation of artificial intelligence applications. Although applications of DNNs to real-world problems have become ubiquitous, there is a lack of understanding of how these circuits are affected by faults. Due to this fact, adoption of DNNs in safety-critical domain has been lagging behind. As there exist no commonly accepted reliability assessment metrics for DNNs, their certification for safety-critical applications is not possible. In this PhD thesis, we are optimising the fault injection process that is a widely used reliability assessment technique for DNNs.