Energy efficient embedded artificial intelligence (E3AI)

This Bordeaux Summer School is open to second year Master students, doctoral students, post-doctoral fellows, researchers and professionals with an interest in embedded artificial intelligence, deep learning, neuromorphic computing, natural language processing, spintronics and 6G communications.

Artificial intelligence and deep learning applications currently require consequent amounts of computing resources that are not easily accessible for low-latency applications handling sensitive data. Massive data sources have to be pushed to multiple high-speed computers in remote server farms, raising serious concerns related to security and privacy. A possible solution comes from executing machine learning models on embedded devices, a topic generally known as “Embedded Artificial Intelligence”. This calls for a close integration of software and hardware solutions with highly optimised machine learning models and a new generation of lightweight, energy-efficient embedded hardware suitable for executing neural networks on edge devices.

Course content will address the consequent skill gap between embedded technology and deep learning applications, and will equip participants with the interdisciplinary knowledge and skills needed to develop innovative circuit architectures and execute data intensive applications on resource-constrained devices.

This summer school is supported by FVLLMONTI, Hermes and RadioSpin, three H2020 European projects, and is co-organised with the Green AI (GrAI) chair in artificial intelligence and the IEEE Circuits and Systems Society. The event is the first in a series of similar bi-annual events organised by and for the Embedded Artificial Intelligence (EAI) transdisciplinary community.