Type de contrat : CDD

Niveau de diplôme exigé : Bac + 5 ou équivalent

Fonction : Doctorant

The Inria center at the University of Rennes is one of eight Inria centers and has more than thirty research teams. The Inria center is a major and recognized player in the field of digital sciences. It is at the heart of a rich ecosystem of R&D and innovation, including highly innovative SMEs, large industrial groups, competitiveness clusters, research and higher education institutions, centers of excellence, and technological research institutes.

Disclaimer

A PhD is not a continuation of coursework or a natural next step after a Master’s degree. A PhD is a long-term, research-focused commitment that requires deep curiosity, self-motivation, resilience, and a certain degree of autonomy.

By research, we mean creating new knowledge, not just applying existing theories. Your task is to discover, design, or prove something that no one has done before, work that will become what future students study.

If you are mainly looking for structured classes, predefined assignments, or a repeat of your Master’s experience, you will likely find this path unfulfilling. We welcome applications from candidates who are excited by uncertainty, driven to ask original questions, and eager to shape the frontier of their field.

Context and Background

The primary objective of this thesis is to investigate and advance the design of energy-efficient AI accelerators by dynamically applying approximate computing techniques and to advance hardware-software co-design methodologies.

The research will build upon recent advancements in efficient domain-specific architectures for AI. The goal is to develop novel approaches that balance performance, energy efficiency, and accuracy, while addressing the unique challenges of implementing approximate computing in real-world AI systems.

This research explores the principles and practical implications of approximate computing as a pathway toward more energy-efficient AI hardware accelerators. It examines how different forms of approximation affect computational efficiency, prediction accuracy, and overall system-level performance. Rather than treating these techniques in isolation, the study considers their combined impact across the computing stack, with particular attention to how accuracy-efficiency trade-offs can be characterized and controlled.

A central theme of the work is the integration of hardware and software perspectives through a co-design approach. By closely aligning algorithmic characteristics with architectural features, the research aims to uncover strategies for embedding approximation mechanisms directly into accelerator designs. Emphasis is placed on adaptive and context-aware approximation techniques that can dynamically balance energy savings and output quality, ensuring that efficiency gains do not compromise application-level requirements.

To ground these ideas in practice, the research involves modeling, simulation, and experimental prototyping using representative AI workloads, including deep learning inference and computer vision applications. Through systematic evaluation and validation, the study aims to assess the feasibility, robustness, and scalability of proposed approaches, contributing insights into the design of next-generation energy-efficient AI systems.

Required Skills

We seek highly motivated and passionate candidates. Autonomy is a highly appreciated quality.

• Subsidized meals
• Partial reimbursement of public transport costs
• Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
• Possibility of teleworking (after 6 months of employment) and flexible organization of working hours
• Professional equipment available (videoconferencing, loan of computer equipment, etc.)
• Social, cultural and sports events and activities

monthly gross salary 2300 euros

Attention: Les candidatures doivent être déposées en ligne sur le site Inria. Le traitement des candidatures adressées par d'autres canaux n'est pas garanti.