As an intern at the CEA, you will have the opportunity to work within a world-renowned research environment. Our teams consist of passionate and dedicated experts, providing an ideal setting for learning and collaboration. You will have access to state-of-the-art equipment and top-tier research resources to carry out your assignments. The work conducted may potentially lead to a scientific publication. Context The goal of this internship is to investigate hybrid performance modeling techniques that combine ML-based data-driven models with structured top-down analytical models. What do we expect from you? Using the CVA6, CVA6S, CVA6S+ architectural evolution as a case study, the internship will explore: Hybrid modeling: Combining simulation ML-driven performance insights with analytical architectural models. Transferability: Evaluating whether models developed for one hardware generation can be adapted to subsequent generations with limited retraining or recalibration. Design-space exploration: Assessing the ability of hybrid models to efficiently predict the impact of architectural modifications. The internship aims to develop and evaluate a hybrid modeling methodology that balances accuracy, expressiveness, and transferability across hardware generations. The ultimate objective is to assess its potential for enabling flexible hardware design-space exploration, reducing reliance on costly cycle-accurate simulations. References [ISCA'25] Concorde: Fast and Accurate CPU Performance Modeling with Compositional Analytical-ML Fusion [ICCAD'23] PANDA: Architecture-Level Power Evaluation by Unifying Analytical and Machine Learning Solutions #Cea-List