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. Using the CVA6, CVA6S, CVA6S+ architectural evolution as a case study, the internship will explore:
What do we expect from you?
<ul>
<li>Hybrid modeling: Combining simulation ML-driven performance insights with analytical architectural models.</li>
<li>Transferability: Evaluating whether models developed for one hardware generation can be adapted to subsequent generations with limited retraining or recalibration.</li>
<li>Design-space exploration: Assessing the ability of hybrid models to efficiently predict the impact of architectural modifications.</li>
</ul>
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
Profil du candidat
Profile :
<ul>
<li>Required level: Master's degree or Engineering diploma</li>
<li>Required Skills: Familiarity with AI, knowledge of computer architecture, proficiency in Python and C/C++, and experience with Git</li>
<li>Other Qualities: Collaborative mindset and a genuine curiosity</li>
</ul>
Localisation du poste
Site
Saclay
Localisation du poste
France, Ile-de-France, Essonne (91)
Ville
Saclay
Critères candidat
Diplôme préparé
Bac+5 - Diplôme École d'ingénieurs
Demandeur
Disponibilité du poste
01/02/2027

Internship -Combining Analytical and ML-based Models for Efficient HW Performance Prediction-Saclay-H/F

Internship -Combining Analytical and ML-based Models for Efficient HW Performance Prediction-Saclay-H/F

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