September 18-22, 2023
Lorient, France
Abstract
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 the performing all of 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 and an Hackathon by the MSCA Rising Stars project.
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.
The lectures and labs will be in English. Non French speakers are welcome.
Program
- Keynote by Rising Stars. Eduardo Quinones (BSC) : Task-based Parallel Programming Models: The Convergence of High-Performance and Edge Computing Domains.
- Hackathon by Rising Stars
- Real-time scheduling analysis of SDF graphs: an example with Cheddar. Hai-Nam Tran
- Hardware Friendly Reinforcement Learning with Tangled Program Graphs. Karol DESNOS
- Parallel Programming for Embedded MPSoCs. Maxime PELCAT
- Design Space Exploration for machine learning hardware accelerators Steven DERIEN + Mickaël DARDAILLON
- Markov Decision Processes for Power/Performance Optimization. Shuvra BHATTACHARYYA
Registration
If you are interested in registering for the summer school, please send an email to Jean-François NEZAN adding your PhD supervisors in copy, the name of your doctoral school, the title of your PhD and attest you understand the travel to/from Lorient for the event are not covered by the summer school.
The event is limited to 20 students so it is suggested that interested students get in contact soon. There is no registration fee and moreover, accommodation during the period September 18-22 is covered by the summer school. To facilitate travel to and from the event, the first and last day are planned as half-days, while the middle day is a full-day.
Venue
Université de Bretagne-Sud, UFR Sciences & Sciences de l’Ingénieur, Lorient
Organization Committee
Kevin Martin, Lab-STICC
Damien Gratadour, Observatoire de Paris
Jean-Francois Nezan, INSA/IETR
Shuvra S. Bhattacharyya, INSA/IETR and UMD
The summer school is organized by INSA/IETR-VAADER, Observatoire de Paris, INRIA-TARAN, INRIA-TEA, Lab-STICC-ARCAD, and LabSTICC-SHAKER, and is sponsored by the MSCA Rising Stars Project, the CominLabs International Chair on Adaptive Machine Learning at the Network Edge and by UBS.
