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Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →To run an AI model on a satellite, design the inference task around the spacecraft’s actual power, memory, thermal, processor, and communications budgets. Then adapt and benchmark the model on representative hardware, qualify the system for the mission environment, and plan a safe way to update or recover the software. There is no universal model-size or wattage limit: those depend on the spacecraft and its compute hardware.
What onboard AI can do—and what it changes
Onboard inference moves some processing close to the sensor. Instead of sending every raw image to Earth, a satellite can analyze data locally and transmit selected detections, derived products, or prioritized images. NASA describes edge processing for near-real-time payload processing and spacecraft autonomy, as well as image compression, in its Small Spacecraft Avionics guidance.
That can make a mission less dependent on downlink capacity, but it does not eliminate communications constraints. The satellite still needs to send useful results, and limited bandwidth can also make post-launch software changes difficult. NASA’s 2026 Prithvi report describes task-specific decoder packages as a way to add a capability with less bandwidth than uploading a complete replacement model.
How to plan a deployment
1. Define the decision the satellite must make
Start with the sensor input, the output the model must produce, the latest useful time for that output, and what the spacecraft should do with it. A model that identifies a flood, flags cloud cover, compresses an image, or supports spacecraft autonomy has a more precise job than a general-purpose model with no defined operational outcome.
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- Specify the input data and its expected range, including relevant sensor and scene conditions.
- Define the output: for example, a detection, a classification, a compressed product, or a priority signal for downlink.
- Set the required latency and how often the model will run. Include the intended duty cycle rather than assuming continuous inference.
- Decide what happens when the output is uncertain, unavailable, or inconsistent with other spacecraft data.
For Earth observation, detecting floods or clouds may help prioritize products for transmission. NASA’s in-orbit Prithvi demonstration tested flood and cloud detection, but those tasks are examples—not a guarantee that another model or mission will perform similarly.
2. Get the spacecraft budgets before choosing a model
Ask the spacecraft and payload teams for the resources actually available to inference. Include average and peak power, available energy over the intended duty cycle, thermal limits, RAM, nonvolatile storage, processor interfaces, and the capacity to recover from faults. Also establish the mission’s downlink opportunities and the amount of data it can devote to software updates.
These are mission-specific limits. The cited NASA and ESA material does not establish a universal satellite wattage, model-size ceiling, or inference budget. A model that fits one processor’s memory can still exceed another spacecraft’s power or thermal limits.
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3. Choose a compute architecture that fits the mission
Compare candidate architectures using the same workload and mission assumptions. CPU-only processing, an accelerator, and a separate coprocessor can differ in throughput, energy use, software support, interfaces, fault handling, and qualification effort. The examples below illustrate approaches documented by NASA and ESA; they are not a controlled performance comparison.
| Approach | Documented example | What to check for your mission |
|---|---|---|
| CPU or payload-processor integration | ESA describes Myriad 2 integration with the CogniSAT-XE1 mission and Jetson-based processing in its Sterna/Morus architecture. | Inference latency and energy on the target processor; supported operations and runtime; memory, interfaces, thermal behavior, and recovery path. The cited sources do not give a common CPU-versus-accelerator benchmark. |
| Dedicated inference coprocessor | NASA’s SC-LEARN describes a CubeSat-sized Edge TPU coprocessor, with high-performance, fault-tolerant, and power-saving modes. | Compatibility between the exported model and accelerator; host interface; mode behavior; integration and qualification requirements. No universal power or model-size limit is stated. |
| Integrated edge-compute architecture | ESA’s ASCEND project describes a radiation-tolerant supervisor separated from a Jetson-based processing domain. | How the supervisor detects faults, controls boot and recovery, and protects mission functions; the processing hardware’s actual mission status and qualification evidence. |
When screening options, compare inference throughput and latency, average and peak power, energy per inference, thermal dissipation, RAM, storage, model-update size, radiation evidence, fault recovery, runtime support, interfaces, integration effort, maturity, and flight heritage. Do not rank processors by advertised throughput alone. ESA’s ASCEND page states that Sterna delivers at least 100 TOPS (INT8) and Morus at least 250 TOPS in its stated configuration; these are project claims, not results from a common benchmark against other devices.
4. Adapt the model for the target hardware
Choose a model suited to the defined task and the operations supported by the selected processor. Then test techniques such as quantization, pruning, distillation, or hardware-aware architecture search where they might reduce memory, latency, or energy use. Each can change task performance, so measure accuracy on mission-relevant data after each change.
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NASA’s SC-LEARN paper describes training and quantizing TensorFlow models for an Edge TPU-based design. That is an example of a hardware-specific workflow, not a universal satellite model format. Confirm the deployment toolchain, supported model operations, and export path with the actual processor and runtime.
5. Benchmark the complete inference path
Run the intended software stack on the representative processor, not only on a desktop or a different development board. Measure the full path from input to stored or transmitted result:
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- Execute the model, including any accelerator handoff.
- Postprocess the output and apply the mission’s decision rules.
- Store, package, or hand off the result for transmission.
Record latency, memory use, energy, thermal behavior, and task quality under representative inputs and operating conditions. ESA Φ-lab’s summary of its 2024 neural-architecture-search project specifically describes hardware-aware profiling for latency, memory, and power. Its reported burned-area segmentation evaluation found a 5.35 MB NAS-generated model with an IoU of 0.870, compared with a 355 MB baseline U-Net with an IoU of 0.794. Those are results reported in that project summary for its described task and evaluation; they do not predict performance on a different model, dataset, or satellite.
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6. Plan updates and recovery before launch
Define the update process before the spacecraft depends on the model. Specify how an update is validated, how its integrity is checked, what happens if transmission is interrupted, and how the spacecraft returns to a known-good software image if a new version fails. Reserve downlink capacity for updates alongside science and operational data.
NASA’s Prithvi report offers one bandwidth-conscious pattern: retain an onboard base model and send a smaller task-specific decoder package when that is appropriate, rather than replacing the whole model. It is an example, not a universal interface or a guarantee that a given satellite can accept the package.
ESA’s ASCEND project page describes A/B boot redundancy and golden-image recovery in its supervisor architecture. Those features show how recovery can be designed into a system; they should not be assumed to exist on unrelated hardware.
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7. Qualify the integrated system for the mission
Assess radiation effects, thermal conditions, vibration, electrical and data interfaces, and mission lifetime for the actual orbit and hardware revision. Check not just whether a processor can run the model, but whether the complete payload and its software can keep operating and recover safely in the intended environment.
ESA’s June 2023 Myriad 2 report describes proton testing for single-event effects and total ionizing dose, and says the results indicated suitability for LEO missions in that activity. ESA also cautions that using a commercial off-the-shelf component for spaceflight requires thorough testing and development for the in-space environment and operating conditions. Those findings do not qualify a different processor, revision, orbit, or spacecraft design.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What current in-orbit examples show
NASA reported in May 2026 that a compressed version of its Prithvi geospatial foundation model was uploaded to the Kanyini satellite and the IMAGIN-e payload on the International Space Station. NASA said flood and cloud detection performance was tested in different computing environments. The report describes Prithvi as trained on data spanning 13 years. This demonstrates an in-orbit geospatial model deployment and task testing; it does not establish a universal model format, processor choice, or power budget.
The example also suggests a useful design option: a mission may keep a capable, suitably compressed base model onboard and add task-specific components when the software architecture, validation process, and available bandwidth permit it. Whether that is practical depends on the chosen hardware and mission operations.
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- Can the selected processor run the full preprocessing-to-output path within the spacecraft’s power, memory, timing, and thermal limits?
- Does the model retain acceptable quality on data representative of the mission after conversion or compression?
- Are the required model operations supported by the processor’s runtime and export tools?
- Can operators validate, upload, and recover software within the mission’s communications and fault-management constraints?
- Is the hardware evidence relevant to the actual orbit, component revision, integration, and expected lifetime?
The cited NASA and ESA examples do not provide a common accuracy-per-watt benchmark for the same workload across processors. A mission-specific evaluation is therefore necessary to choose among otherwise viable options.
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