About
Deployment of edge-AI systems empowered with accurate DNNs for feature extraction is the most promising approach to reduce the burden of high-volume data load for satellites, ground-stations and ground-processing servers.
The EU funded project Edge SpAIce unites expertise from four partners. Agenium Space a French company expert in AI solutions in Space, CERN, EnduroSat, a Bulgarian satellite builder and operator, and the remote sensing department of the National Technical University of Athens. The consortium is building an Edge-AI system that can reduce satellites' data downlink latency along and eventually opening the path towards development of novel and competitive EO space-based value-added services (space-VAS).
However, downsizing techniques for complex DNNs show strong limitations in retaining original processing accuracy, which hindered edge-AI applicability. As such, inability to produce small size yet accurate DNNs in the past has allowed deployment of only very basic image segmentation NNs on-board satellites, which has become a bottleneck towards development of more intelligent space-VAS.
Edge SpAIce's approach aims at hatching full potential of edge-AI technology by drastically improving NN architectures reduction efficacy to prevent accuracy loss and bolster AI technology deployment in space by demonstrating its application in a challenging use-case scenario for marine plastic litter detection.
To achieve this, Edge SpAIce will develop a DNN (50-100M parameters) starting from publicly available database on remote marine plastic litters detection adopted and amended to actual target satellite, in parallel improving capabilities of both Agenium Space's distillation tool and CERN's open-source HLS4ML tool for accurate DNN optimisation and deployment onto SoC-FPGAs used in space.
During this seminar, each partner will present and describe his part of the development, followed by Q&A
To be kept informed of KT Seminars please sign up at:…
The talk
Credits and licence
- Institution
- CERN
- Department
- KT Seminars
- Taught by
- Joseph Thuilier, Nick Ziogas, Sioni Paris Summers, Francois de Vieilleville, Simon Vellas
- Year
- 2024
- Language
- English
- Source
- CERN
- Licence
- CERN, as CERN states it. CERN's terms
- Attribution
- © CERN. CERN provides its audiovisual media free of charge for educational and informational use, credited to CERN; this lecture is shown in CERN's own player.
- Who can watch
- CERN gives no rating. Invisible Network shows it to Teens profiles and older, judged from what the source says about it. How courses are placed