Evaluating quantile regression neural networks for optimizing real-time applications on heterogeneous platforms

Modern cyber-physical systems increasingly rely on computationally demanding applications, particularly at the edge, where Artificial Intelligence-based algorithms are deployed. To meet these demands, industry trends are shifting towards heterogeneous MultiProcessor Systems on Chip (MPSoCs), which must also satisfy strict real-time and functional safety requirements. A major challenge in such systems is memory contention, where multiple processing units compete for shared memory resources, affecting application performance and the accurate estimation of Worst-Case Execution Times (WCETs). Traditional static analysis becomes impractical as system configurations grow in complexity. This work presents the design of an analysis and optimization framework for real-time systems that re-evaluates WCET estimates based on system configurations to reflect the impact of memory contention on heterogeneous platforms. The proposed method estimates new WCETs using Quantile Regression Neural Networks (QRNNs), which infer memory contention from Event Monitor data. Experimental results reveal that QRNN models must be system-specific for accurate predictions and that memory access patterns significantly affect model generalization. Two strategies are proposed: using generic models for simplicity or task-specific models for higher accuracy. Despite some potential underestimations, QRNNs maintain a strong correlation with actual observed contention, enabling effective worst-case scenario identification. Furthermore, a comparative analysis highlights the superior scalability of the estimation-based approach over empirical measurements, especially in large system optimization processes where performance can be easily enhanced by at least two orders of magnitude, making it a practical solution for real-time system design and analysis.