Machine Learning-Based Inference of Abstract Behavioural Models From Execution Traces in System-on-Chip Environments

System-on-Chip (SoC) platforms, being a part of contemporary embedded and multicore systems, must be complemented by strong behavioural modeling to verify, validate, and debug them. This work proposes AutoModel ML, a novel machine learning-assisted method that learns high-level behavioural models directly from SoC execution traces. Our pipeline, which combines recurrent neural networks with causality analysis based on constraints, constructs annotated causality graphs initially. They are converted into symbolic constraints and solved by SAT/SMT in an attempt to achieve compact finite state abstractions, like methods such as AutoModel. Empirical evaluation on synthetic benchmarks and real-world multi core SoC workloads (via gem5) demonstrates some significant enhancements: automated causality discovery among messages, recovering over 90% of known protocol traces; ML-augmented abstraction synthesis with a fidelity-explainability tradeoff; counterexample-guided iterative refinement with ~30% improvement in model completeness; and real-world application in protocol verification, anomaly detection, and hardware debugging. Our results indicate that AutoModel ML not only improves upon conventional trace mining methods in terms of scalability and precision, but also generates explainable models with the ability to generalize beyond seen executions. Through the significant diminishment of the amount of manual model engineering required, this system makes scalable, automated SoC behavioral modeling an attainable goal, a step toward cognitive, ML-driven hardware verification.