  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.
