On-board decision making in space with deep neural networks and risc-v vector processors

On-board decision making in space with deep neural networks and risc-v vector processors

Di Mascio, S. (TU Delft Space Systems Egineering)
Menicucci, A. (TU Delft Space Systems Egineering)
Gill, E.K.A. (TU Delft Space Systems Egineering)
Furano, Gianluca (European Space Agency (ESA))
Monteleone, Claudio (European Space Agency (ESA))

2021

The use of deep neural networks (DNNs) in terrestrial applications went from niche to widespread in a few years, thanks to relatively inexpensive hardware for both training and inference, and large datasets available. The applicability of this paradigm to space systems, where both large datasets and inexpensive hardware are not readily available, is more difficult and thus still rare. This paper analyzes the impact of DNNs on the system-level capabilities of space systems in terms of on-board decision making (OBDM) and identifies the specific criticalities of deploying DNNs on satellites. The workload of DNNs for on-board image and telemetry analysis is analyzed, and the results are used to drive the preliminary design of a RISC-V vector processor to be employed as a generic platform to enable energy-efficient OBDM for both payload and platform applications. The design of the memory subsystem is carried out in detail to allow full exploitation of the computational resources in typically resource-constrained space systems.

http://resolver.tudelft.nl/uuid:c4edbeb1-5448-43c9-b43b-cf9e5ea0cd96

https://doi.org/10.2514/1.I010916

2327-3097

Journal of Aerospace Information Systems (online), 18 (8), 553-570

Institutional Repository

journal article

© 2021 S. Di Mascio, A. Menicucci, E.K.A. Gill, Gianluca Furano, Claudio Monteleone