  This paper investigates the application of eXplainable Artificial
Intelligence (XAI) in the design of embedded systems using machine learning
(ML). As a case study, it addresses the challenging problem of static silent
store prediction. This involves identifying redundant memory writes based only
on static program features. Eliminating such stores enhances performance and
energy efficiency by reducing memory access and bus traffic, especially in the
presence of emerging non-volatile memory technologies. To achieve this, we
propose a methodology consisting of: 1) the development of relevant ML models
for explaining silent store prediction, and 2) the application of XAI to
explain these models. We employ two state-of-the-art model-agnostic XAI methods
to analyze the causes of silent stores. Through the case study, we evaluate the
effectiveness of the methods. We find that these methods provide explanations
for silent store predictions, which are consistent with known causes of silent
store occurrences from previous studies. Typically, this allows us to confirm
the prevalence of silent stores in operations that write the zero constant into
memory, or the absence of silent stores in operations involving loop induction
variables. This suggests the potential relevance of XAI in analyzing ML models'
decision in embedded system design. From the case study, we share some valuable
insights and pitfalls we encountered. More generally, this study aims to lay
the groundwork for future research in the emerging field of XAI for embedded
system design.
