Sem4EDA: A Knowledge-Graph and Rule-Based Framework for Automated Fault Detection and Energy Optimization in EDA-IoT Systems ^†

This paper presents Sem4EDA, an ontology-driven and rule-based framework for automated fault diagnosis and energy-aware optimization in Electronic Design Automation (EDA) and Internet of Things (IoT) environments. The escalating complexity of modern hardware systems, particularly within IoT and embedded domains, presents formidable challenges for traditional EDA methodologies. While EDA tools excel at design and simulation, they often operate as siloed applications, lacking the semantic context necessary for intelligent fault diagnosis and system-level optimization. Sem4EDA addresses this gap by providing a comprehensive ontological framework developed in OWL 2, creating a unified, machine-interpretable model of hardware components, EDA design processes, fault modalities, and IoT operational contexts. We present a rule-based reasoning system implemented through SPARQL queries, which operates atop this knowledge base to automate the detection of complex faults such as timing violations, power inefficiencies, and thermal issues. A detailed case study, conducted via a large-scale trace-driven co-simulation of a smart city environment, demonstrates the framework’s practical efficacy: by analyzing simulated temperature sensor telemetry and Field-Programmable Gate Array (FPGA) configurations, Sem4EDA identified specific energy inefficiencies and overheating risks, leading to actionable optimization strategies that resulted in a 23.7% reduction in power consumption and 15.6% decrease in operating temperature for the modeled sensor cluster. This work establishes a foundational step towards more autonomous, resilient, and semantically-aware hardware design and management systems.