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The revised GIASaaS (Global Instant Satellite as a Service, formerly Greek Infrastructure for Satellite as a Service) concept website by OHB Hellas is finally back online - and Akida now no longer shows up as UAV1 (see my post above) but as one of three SATs! đ°
AKIDA, the envisioned SAT with the AKD1000 PCIe Card onboard, is slated to handle both Object Detection as well as Satellite Detection, while the planned KRIA and CORAL satellites (equipped with a Xilinx KRIA KV260 Vision AI Starter Kit resp. a Google Coral TPU Dev Board) are tasked to handle Vessel Detection, Cloud Detection and Fire Detection (for some reason OHB Hellas removed Flood Detection from the list of applications).
Please note that this is currently a concept website only.
The idea behind GIASaaS as well as mock 3D-printed versions of the yet-to-be-lauched satellites were presented at DEFEA 2025 - Defense Exhibition Athens earlier this month, as @Stable Genius had spotted on LinkedIn:
GIASaaS by OHB Hellas
giasaas.eu
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Evgenios Tsigkanos from OHB Hellas presented GIASaaS
#edhpc2025 #edgeai #esa #onboardprocessing #aiinspace #kubernetes #mlops #satelliteasaservice | OHB Hellas
đ Today at EDHPC 2025, Evgenios Tsigkanos presented our approach to Satellite-as-a-Service, orchestrating ML workloads across various edge hardware platforms like a cloud cluster. đ Our demo highlighted applications such as vessel, cloud, flood, and fire detection. We harnessed the power of...
Gilles Bézard and Alf Kuchenbuch were also going to refer to GIASaaS in their two-part tutorial at EDHPC 2025 yesterday:
Here is more info on the two-part tutorial Gilles Bézard and Alf Kuchenbuch will be presenting on 13 October at EDHPC25 in Elche, Spain:
[All the weird extra os under âUse cases for Neuromorphic Processing in spaceâ are evidently bullet points gone rogue in the website layout⊠When I copy and paste that passage from below website, the combination âspace:â followed by âoâ shows up as âspaceâ here on TSE
]
Tutorials - 25M02 - EDHPC 2025 - Centro De Congresos De Elche
atpi.eventsair.com
Neuromorphic AI in Space Tutorials (by BrainChip)
Introduction
In space applications, every milliwatt matters. Satellites rely on ultra-low-power chips to process data on-board, where sending everything back to Earth is often impossible or inefficient. This makes efficient machine learning deployment on embedded hardware not just useful, but essential.
The goal of the tutorials is to show how advanced AI capabilities can be packed into tiny, power-constrained devices, enabling smarter, faster, and more autonomous satellite systems.
Audience
Project managers (particularly part 1) and engineers working on embedded low-power AI applications.
Level: from beginner to expert.
What will you learn?
In part 1: Akida in Space â Bringing Autonomy, Robustness and Efficient Data Transmission to Space Vehicles, you will learn:
- Why AI in Space?
- Why neuromorphic AI in Space?
- BrainChip Akida IP is the only Event-based AI on the commercial market â IP and silicon
- Use cases for Neuromorphic Processing in space
Lunar landingo Docking in spaceo Earth observationo Space Situational Awarenesso Satellite detection (use case OHB Giasaas)
In part 2: Bringing AI to the Edge â End-to-End Machine Learning Deployment on an Embedded Low-Power AI Neuromorphic HW, you will learn:
- Fetch and prepare a dataset suitable for your target application.
- Design and train a machine learning model using TF/Keras.
- Apply quantization techniques to reduce model size and optimize it for embedded hardware.
- Convert the trained model into a format compatible with Akida hardware toolchain.
- Export the model as a C source file that can be included and compiled together with your C main application.
- Integrate the Akida model binary into a baremetal C application running on a STM32 microcontroller.
- Run inference on Akida ultra-low-power hardware device, demonstrating efficient on-board processing for space-constrained environments such as satellites.
Outline of hands-on tutorial
In the first tutorial, we teach why there is a paradigm shift in Space from purely deterministic classic programming to the use of low power AI in specific use cases. We show the vast improvement in capabilities coming with the use of AI in Space.
In the second tutorial, we demonstrate how to take an ML model from dataset preparation all the way to baremetal deployment on a microcontroller with a hardware accelerator. By the end, you will know how to take an ML project from concept to fully optimized embedded deployment, step by step.
Speakers:
- Gilles BĂ©zard (BrainChip) â Part 1 & 2
- Alf Kuchenbuch (BrainChip) â Part 1
As you can see, Gilles BĂ©zard and Alf Kuchenbuch will also be referring to the GIASAAS (Global Instant Satellite as a Service) concept by OHB Hellas, which I first posted about yesterday a year agoâŠ
https://thestockexchange.com.au/threads/brn-discussion-ongoing.1/post-438109
⊠and gave an update on in May:
To the best of my knowledge, this is the first time BrainChip will be making a reference to GIASAAS.