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

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Onsite

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

CEA

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

Skills

PythonMachine LearningAI

9 days ago

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Important information


Contract type:

Internship

Location:

91400 Saclay, France

Work mode:

Onsite

Published on:

17 October 2026

What they need


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

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Profil du candidat Profile:

  • Required level: Master's degree or Engineering diploma
  • Required Skills: Familiarity with AI, knowledge of computer architecture, proficiency in Python and C/C++, and experience with Git
  • Other Qualities: Collaborative mindset and a genuine curiosity

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

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