# AI in Space for Scientific Missions: Strategies for Minimizing Neural-Network Model Upload

arXiv: https://arxiv.org/abs/2406.14297 · HTML: https://mlsanity.com/paper/2406.14297/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 31 references that are in the map.
- First posted on arXiv 20 Jun 2024.

## Builds on (most cited first, 12 of 12)

- [In-Datacenter Performance Analysis of a Tensor Processing Unit](https://mlsanity.com/paper/1704.04760/) (2017)
- [Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations](https://mlsanity.com/paper/1609.07061/) (2016)
- [Deep Learning with Limited Numerical Precision](https://mlsanity.com/paper/1502.02551/) (2015)
- [Pruning and Quantization for Deep Neural Network Acceleration: A Survey](https://mlsanity.com/paper/2101.09671/) (2021)
- [A Study of BFLOAT16 for Deep Learning Training](https://mlsanity.com/paper/1905.12322/) (2019)
- [NVIDIA Tensor Core Programmability, Performance & Precision](https://mlsanity.com/paper/1803.04014/) (2018)
- [The Final Frontier: Deep Learning in Space](https://mlsanity.com/paper/2001.10362/) (2020)
- [Deployment of Deep Neural Networks for Object Detection on Edge AI Devices with Runtime Optimization](https://mlsanity.com/paper/2108.08166/) (2021)
- [Automated classification of plasma regions using 3D particle energy distributions](https://mlsanity.com/paper/1908.05715/) (2019)
- [Approximation of functions with one-bit neural networks](https://mlsanity.com/paper/2112.09181/) (2021)
- [Inference of Recyclable Objects with Convolutional Neural Networks](https://mlsanity.com/paper/2104.00868/) (2021)
- [Exploring the Vision Processing Unit as Co-processor for Inference](https://mlsanity.com/paper/1810.04150/) (2018)

## Built on by (most cited first, 3 of 3)

- [FPGA-Based Neural Network Accelerators for Space Applications: A Survey](https://mlsanity.com/paper/2504.16173/) (2025)
- [Evaluating Four FPGA-accelerated Space Use Cases based on Neural Network Algorithms for On-board Inference](https://mlsanity.com/paper/2603.14091/) (2026)
- [Learning in Orbit: Onboard Training and Closed-Loop Attitude Control with a Thermal Segmentation Model on a Spacecraft](https://mlsanity.com/paper/2610.19122/) (2026)

## Abstract (arXiv)

Artificial Intelligence (AI) has the potential to revolutionize space exploration by delegating several spacecraft decisions to an onboard AI instead of relying on ground control and predefined procedures. It is likely that there will be an AI/ML Processing Unit onboard the spacecraft running an inference engine. The neural-network will have pre-installed parameters that can be updated onboard by uploading, by telecommands, parameters obtained by training on the ground. However, satellite uplinks have limited bandwidth and transmissions can be costly. Furthermore, a mission operating with a suboptimal neural network will miss out on valuable scientific data. Smaller networks can thereby decrease the uplink cost, while increasing the value of the scientific data that is downloaded. In this work, we evaluate and discuss the use of reduced-precision and bare-minimum neural networks to reduce the time for upload. As an example of an AI use case, we focus on the NASA's Magnetosperic MultiScale (MMS) mission. We show how an AI onboard could be used in the Earth's magnetosphere to classify data to selectively downlink higher value data or to recognize a region-of-interest to trigger a burst-mode, collecting data at a high-rate. Using a simple filtering scheme and algorithm, we show how the start and end of a region-of-interest can be detected in on a stream of classifications. To provide the classifications, we use an established Convolutional Neural Network (CNN) trained to an accuracy >94%. We also show how the network can be reduced to a single linear layer and trained to the same accuracy as the established CNN. Thereby, reducing the overall size of the model by up to 98.9%. We further show how each network can be reduced by up to 75% of its original size, by using lower-precision formats to represent the network parameters, with a change in accuracy of less than 0.6 percentage points.