Optimizing Neural Networks for Space Missions to Enhance Data Collection

Artificial Intelligence (AI) is being explored for its potential to enhance space exploration by allowing spacecraft to make autonomous decisions. A recent paper titled "AI in Space for Scientific Missions: Strategies for Minimizing Neural-Network Model Upload" by Jonah Ekelund and colleagues discusses strategies to optimize the use of neural networks onboard spacecraft, particularly in the context of NASA's Magnetospheric MultiScale (MMS) mission.

The research highlights the challenges associated with limited bandwidth for satellite uplinks, which can make data transmission costly. The authors propose that smaller neural networks can significantly reduce uplink costs while improving the quality of scientific data collected. They evaluate the use of reduced-precision and minimal neural networks to decrease upload times.

One key finding is that a convolutional neural network (CNN) can be effectively reduced to a simpler model without a significant loss in accuracy. The study demonstrates that the size of the model can be reduced by up to 98.9% while maintaining an accuracy greater than 94%. This reduction is achieved through a combination of model simplification and lower-precision parameter representation, which can decrease the model size by up to 75% with minimal impact on performance.

The implications of this research are significant for future space missions. By enabling more efficient data processing and transmission, these advancements could enhance the ability of spacecraft to collect and analyze valuable scientific data in real-time, ultimately contributing to more effective exploration of the Earth's magnetosphere and beyond.

The full paper can be accessed at arXiv:2406.14297.