WP2 develops machine learning algorithms for autonomous data processing in space missions, including smart data management, solar image analysis, and space radiation prediction. These advancements enhance scientific operations and improve AI-driven space research.
WP3 develops a specialized hardware accelerator to execute AI/ML algorithms in space, optimizing performance and power efficiency using FPGA technology. Advanced design techniques ensure the accelerator meets the demanding requirements of in-flight space applications.
WP4 develops a software framework to port machine learning models onto FPGA prototypes and explores both application-dependent and application-independent (soft-GPU) designs. It also defines and executes a validation plan to test the performance of these solutions in space mission scenarios.
WP5 develops a flight simulator to support the implementation and validation of Machine Learning (ML) algorithms for space mission operations. The validation process combines synthetic data with real mission data to refine ML algorithms for in-flight use, and the simulator and tools will be released in Python for public access and further development.
Work Package One provides the overarching management function for the ASAP project.
Work Package Six handles the dissemination activities for the ASAP Project. This includes the website, social media presence (LinkedIn), organizing workshops and summer schools.
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