Annonce

Lorient, 13-16 June 2022

https://project.inria.fr/chairaml/summer-school-on-embedded-signal-processing-and-machine-learning-for-edge-intelligence/

Applications of artificial intelligence (AI), such as those driven by deep learning, are having great impact on numerous sectors of societal importance, such as healthcare, transportation, agriculture, and computer security. An important trend in deployment of AI applications and systems is the migration of signal and information processing functionality closer to the point at which the data is generated or captured — that is, migration toward the network edge as opposed to performing all of the computation on centralized cloud servers. Edge processing offers various potential advantages, including the potential to greatly reduce delays associated with network communication, enhance privacy, and improve reliability and predictability in scenarios where network performance exhibits significant variation.

This summer school will cover key concepts and methods in design and implementation of edge processing systems for AI applications. The summer school will involve lectures and hands-on laboratory sessions on topics that include parallel programming for embedded multiprocessor systems-on-chip (MPSoCs); technology, architecture and organization of memories used at the edge; hardware accelerators for edge processing; real-time scheduling analysis; hardware-friendly reinforcement learning; and Markov decision processes for power/performance optimization.

The summer school will include a keynote lecture by Prof. Marilyn Wolf, Elmer E. Koch Professor of Engineering and Director of the School of Computing at the University of Nebraska-Lincoln.

The summer school is targeted primarily to Ph.D. students and early-stage post-doctoral researchers. Participants will have opportunities to present their research, including both work-in-progress or published research, in poster sessions at the event.