Reliability and power consumption models of embedded systems with a machine learning-based optimization

Most real-time systems are embedded in portable, battery-powered devices that have strict limitations on power consumption. Safety-critical embedded systems, in particular, demand a high level of reliability. To effectively enhance both reliability and power consumption, it is crucial to consider both criteria with an accurate and stable model. Existing research on power and reliability models for embedded systems often lacks the accuracy required for safety-critical applications and fails to account for all hardware and software components. This paper proposes a machine learning-based optimization model designed to improve the accuracy and stability of reliability and power consumption assessments. The proposed model demonstrates a significant enhancement in accuracy compared to previous randomization models, showing a [Formula: see text] improvement in reliability and a [Formula: see text] improvement in power consumption relative to existing state-of-the-art models.