The Machine Learning Application Benchmark Hardware Platform
The Machine Learning Application Benchmark Hardware Platform2023 M. Werner

Machine Learning Application Benchmark

Project Description

The MLAB project concentrates on benchmarking of artificial intelligence models in constrained edge computing cases with a focus on onboard processing for Earth observation satellite systems. Within this ESA activity, our project focuses on benchmarking of system parameters (energy consumption, redundancy, space fill rates, communication properties) for selected computer vision challenges (Airbus Ship Detection Challenge, EuroSAT, Wildfire Detection, etc.) in the context of specific Xilinx hardware, including UltraScale+ architecture system-on-chip (SoC) designs. In this context, we further focus on the Xilinx Deep Learning Processing Unit (DPU) as well as the FINN system and questions of how models from the wider field need to be updated, constrained, quantized, pruned, and simplified in order to reach deployability on (preferrably) space-grade FPGAs. Further, we have a look at how small we can drive our models in order to gain a soft version of radiation-hardness from spatial on-chip diversity by deploying the same model (or variations with different weights) and combining results in voting schemes. As a side-effect, this project aims at generating a huge wildfire detection dataset on various ESA Sentinel missions in collaboration with OroraTech, which is supposed to become openly accessible and immense (currently about 500TB of raw data) focusing on well-documented wildfires in the United States of America.


  1. Krstova, A., Hegwein, F., Lera, M. C. D., Ales, F., Petry, M., Ali, R., Mallah, M., Hili, L., Ghiglione, M., & Werner, M. (2023). On-Board Anomaly Detection on a Flight-Ready System. EDHPC 2023 - European Data Handling and Data Processing Conference. [BibTeX]
  2. Dax, G., & Werner, M. (2022). The Role of Compression in Spatial Computing. PhD Colloquium of the Deutsche Geodätische Kommission, Section on Geoinformatics. [PDF] [BibTeX]
  3. Denizoglu, D. G., Dax, G., Nagarajan, S., Zhang, N., & Werner, M. (2022). Global Active Fire Detection – Towards a SAR-enabled Multi-Sensor Global Monitoring System. Living Planet Symposium 2022. [PDF] [BibTeX]
  4. Raoofy, A., Dax, G., Serra, V., Ghiglione, M., Werner, M., & Trinitis, C. (2022). Benchmarking and Feasibility Aspects of Machine Learning in Space Systems. Proceedings of the 19th ACM International Conference on Computing Frontiers (CF’22). [PDF] [Online] [BibTeX]
  5. Ghiglione, M., Serra, V., Raoofy, A., Dax, G., Trinitis, C., Werner, M., Schulz, M., & Furano, G. (2022). Survey of frameworks for inference of neural networks in space data system. Data Systems in Aerospace (DASIA). Eurospace. [PDF] [BibTeX]
  6. Ghiglione, M., Raoofy, A., Dax, G., Furano, G., Wiest, R., Trinitis, C., Werner, M., Schulz, M., & Langer, M. (2021). Machine Learning Application Benchmark for In-Orbit On-Board Data Processing. European Workshop on On-Board Data Processing. [PDF] [Online] [BibTeX]
  7. Raoofy, A., Dax, G., Ghiglione, M., Langer, M., Trinitis, C., Werner, M., & Schulz, M. (2021). Benchmarking Machine Learning Inference in FPGA-based Accelerated Space Applications. Proceedings of the Workshop on Benchmarking Machine Learning Workloads Co-Located with IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS). [PDF] [BibTeX]


Professur für
Big Geospatial Data Management

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© 2020 M. Werner