Explainable AI for Embedded Systems Design: A Case Study of Static Redundant NVM Memory Write Prediction

arXiv:2403.04337v1 Announce Type: new
Abstract: 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 …

abstract application artificial artificial intelligence arxiv case case study cs.lg cs.pl cs.se design embedded explainable ai explainable artificial intelligence intelligence machine machine learning memory paper prediction store study systems type xai