  Artificial Intelligence (AI) and, in particular, Machine Learning (ML) have
emerged to be utilized in various applications due to their capability to learn
how to solve complex problems. Over the last decade, rapid advances in ML have
presented Deep Neural Networks (DNNs) consisting of a large number of neurons
and layers. DNN Hardware Accelerators (DHAs) are leveraged to deploy DNNs in
the target applications. Safety-critical applications, where hardware
faults/errors would result in catastrophic consequences, also benefit from
DHAs. Therefore, the reliability of DNNs is an essential subject of research.
In recent years, several studies have been published accordingly to assess the
reliability of DNNs. In this regard, various reliability assessment methods
have been proposed on a variety of platforms and applications. Hence, there is
a need to summarize the state of the art to identify the gaps in the study of
the reliability of DNNs. In this work, we conduct a Systematic Literature
Review (SLR) on the reliability assessment methods of DNNs to collect relevant
research works as much as possible, present a categorization of them, and
address the open challenges. Through this SLR, three kinds of methods for
reliability assessment of DNNs are identified including Fault Injection (FI),
Analytical, and Hybrid methods. Since the majority of works assess the DNN
reliability by FI, we characterize different approaches and platforms of the FI
method comprehensively. Moreover, Analytical and Hybrid methods are propounded.
Thus, different reliability assessment methods for DNNs have been elaborated on
their conducted DNN platforms and reliability evaluation metrics. Finally, we
highlight the advantages and disadvantages of the identified methods and
address the open challenges in the research area.
