Speaker
Description
New generations of spacecrafts are required to perform tasks with an increased level of autonomy. Future missions such as Active Debris Removal will rely on novel high-performance avionics to support image processing and Artificial Intelligence algorithms with large workloads. Similar requirements come from Earth Observation applications, where data processing on-board can be critical in order to provide real-time reliable information to Earth.
These new advanced Space applications together with the increase in data amount and processing power, has brought new challenges with it: low determinism, excessive power needs, data losses and large response latency.
In the KATESU project, a novel approach to on-board artificial intelligence (AI) is presented that is based on state-of-the-art academic research of the well known technique of data pipeline. The approach presented here provides a two-dimensional threading model combination of pipeline and parallelization techniques applied to deep neural networks (DNN), making these type of AI applications much more efficient and reliable. This new approach has been validated with several DNN models and two different computer architectures. The results show that the data processing rate and power saving of the applications increase substantially with respect to standard AI solutions.
