Description de l'offre
This internship aims to develop a methodology for constructing machine learning (ML)-based models that effectively generalize across the design space of the CVA6 processor. The objective is to predict performance, power, and area (PPA) metrics based on hardware configurations while reducing the number of required simulations. One aspect of the internship involves conducting a comprehensive state-of-the-art (SoA) review of advanced ML techniques, including Generative Adversarial Networks (GANs) for data augmentation, active learning for efficient simulation selection, and regression models for predictive analysis.
The intern will:
If time allows, the intern may also explore using the developed model for architectural exploration to efficiently identify optimized configurations.
Profil du candidat
Required Level: Master's degree or Engineering diploma
Duration: 6 months
Skills Required: Familiarity with AI, knowledge of Computer Architecture, proficiency in Python and C/C++, and experience with Git
Other Qualities: Strong command of English, collaborative mindset, and a genuine curiosity
Application Materials: Please submit a CV together with academic transcripts and a cover letter
In line with CEA's commitment to integrating people with disabilities, this job is open to all.
