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.

翻译：本文研究了可解释人工智能在基于机器学习的嵌入式系统设计中的应用。通过一个案例研究，我们探讨了静态静默存储预测这一具有挑战性的问题——即仅基于静态程序特征识别冗余内存写入。消除此类写入操作可减少内存访问和总线流量，从而提升性能和能效，尤其是在新兴非易失性内存技术背景下。为此，我们提出了一种方法论，包括：1）开发用于解释静默存储预测的相关机器学习模型；2）应用可解释人工智能来解释这些模型。我们采用两种先进的模型无关可解释人工智能方法分析静默存储的成因。通过案例研究，我们评估了这些方法的有效性。研究发现，这些方法能够为静默存储预测提供解释，且这些解释与先前研究中已知的静默存储成因一致。这通常使我们能够确认将零常量写入内存的操作中静默存储的高发性，或涉及循环归纳变量的操作中静默存储的缺失性。这表明可解释人工智能在分析嵌入式系统设计中机器学习模型决策方面具有潜在价值。通过案例研究，我们分享了一些有价值的见解及遇到的陷阱。更广泛而言，本研究旨在为可解释人工智能在嵌入式系统设计这一新兴领域的未来研究奠定基础。