  Machine Learning (ML) is currently being exploited in numerous applications
being one of the most effective Artificial Intelligence (AI) technologies, used
in diverse fields, such as vision, autonomous systems, and alike. The trend
motivated a significant amount of contributions to the analysis and design of
ML applications against faults affecting the underlying hardware. The authors
investigate the existing body of knowledge on Deep Learning (among ML
techniques) resilience against hardware faults systematically through a
thoughtful review in which the strengths and weaknesses of this literature
stream are presented clearly and then future avenues of research are set out.
The review is based on 220 scientific articles published between January 2019
and March 2024. The authors adopt a classifying framework to interpret and
highlight research similarities and peculiarities, based on several parameters,
starting from the main scope of the work, the adopted fault and error models,
to their reproducibility. This framework allows for a comparison of the
different solutions and the identification of possible synergies. Furthermore,
suggestions concerning the future direction of research are proposed in the
form of open challenges to be addressed.
