Energy Efficient Embedded Artificial Intelligence

About

The E3AI Bordeaux Summer School is open to second year Master students, PhD students, post-doctoral researchers, professionals and experts interested in embedded artificial intelligence, deep learning, neuromorphic computing, natural language processing, ferroelectric technologies and energy-efficient artificial intelligence hardware.

This summer school aims to bridge the skill gap between embedded technology and artificial intelligence applications, by equipping participants with interdisciplinary knowledge in circuit architectures, efficient neural network hardware and resource-constrained artificial intelligence applications.

The 2025 edition is supported by the FVLLMONTI, FerroFutures, FIXIT and Ferro4EdgeAI Horizon Europe projects. This event represents the second edition of a bi-annual gathering for the "Embedded Artificial Intelligence" research community, fostering collaboration between hardware and software experts

Target group

The course is designed for second year Master students, PhD students, post-doctoral researchers, professionals and experts interested in embedded artificial intelligence, deep learning, neuromorphic computing, natural language processing, ferroelectric technologies and energy-efficient artificial intelligence hardware.

Classes are conducted in English. Candidates should have a B2 level of English or equivalent.

Candidates must provide a CV, a short abstract of their research project and cover letter detailing their scientific interest for attending the summer school and the relevance of attending to their professional project

Course aim

The educational objectives of this summer school reflect the interdisciplinary nature of embedded artificial intelligence, focusing on:

- Fabrication, characterisation and modeling of AI-dedicated hardware
- System design, simulation and hardware/software co-design for AI applications
- Application-specific topics, including speech and text processing and AI-powered autonomous systems

The core goal of this summer school is to explore hardware-related aspects of neural networks while equipping participants with the necessary foundational knowledge for their implementation. Topics covered will include:

- Introduction to neural networks and their adaptation to hardware constraints
- Hardware-optimised AI models for embedded and edge computing
- Ferroelectric materials for low-power AI acceleration
- Electrical characterisation of energy-efficient AI devices
- Logic cell design and memory architectures for AI hardware
- System-level simulation and AI hardware exploration
- TCAD modelling and compact device simulations in 3D layouts
- Fabrication of vertical Gate-All-Around (GAA) transistors for AI acceleration
- AI-enhanced circuit design for next-generation computing architectures
- Transformer architectures for speech and natural language processing
- Hardware-based security: AI applications in device authentication and RF signature recognition
- Ferroelectric-based architectures for energy-efficient computing
- AI applications in biomedical signal analysis and cancer detection

The programme is highly interactive, combining lectures, hands-on workshops, system simulations and real-time participatory discussions to maximise learning outcomes. Participants will gain both theoretical knowledge and practical skills, enabling them to design, implement, and optimise hardware-accelerated AI solutions for resource-constrained applications

Fee info