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

CEA Industrie
Saclay, France
29 days ago
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Role details

Contract type
Internship / Graduate position
Employment type
Full-time (> 32 hours)
Experience level
Internship
Working hours
Regular working hours

Tech stack

Artificial Intelligence C++ (Programming Language) Python (Programming Language) Machine Learning System on a Chip Git

Job description

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

Requirements

Within the CEA List, the Electronics Design Automation and Architectures Laboratory (LECA) has the mission to design innovative and flexible system-on-chip architectures that meet the challenges of performance, cost, energy consumption, safety and security, targeting critical embedded systems and HW accelerators for embedded AI. To reduce the development time and improve the quality of these architectures, the team of experts develop innovative design tools and methods., * 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

Benefits & conditions

Durée du contrat (en mois)

6 mois

About the company

Entité de rattachement

Le CEA est un acteur majeur de la recherche, au service des citoyens, de l’économie et de l’Etat.

Il apporte des solutions concrĂštes Ă  leurs besoins dans quatre domaines principaux : transition Ă©nergĂ©tique, transition numĂ©rique, technologies pour la mĂ©decine du futur, dĂ©fense et sĂ©curitĂ© sur un socle de recherche fondamentale. Le CEA s’engage depuis plus de 75 ans au service de la souverainetĂ© scientifique, technologique et industrielle de la France et de l’Europe pour un prĂ©sent et un avenir mieux maĂźtrisĂ©s et plus sĂ»rs.

ImplantĂ© au cƓur des territoires Ă©quipĂ©s de trĂšs grandes infrastructures de recherche, le CEA dispose d’un large Ă©ventail de partenaires acadĂ©miques et industriels en France, en Europe et Ă  l’international.

Les 20 000 collaboratrices et collaborateurs du CEA partagent trois valeurs fondamentales, The French Atomic Energy and Alternative Energies Commission (CEA) is a major player in research, development and innovation. This technological research organization is active in three main areas: energy, information and health technologies, and defense. Recognized as an expert in its fields, CEA is fully integrated into the European research area and is expanding its presence internationally. The Laboratory for Systems and Technology Integration (LIST), located in the southern Île-de-France region (Saclay), has the mission of contributing to technology transfer and promoting innovation in the field of parallel computing systems., 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.

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Apply on www.emploi.cea.fr
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