On-Board Deep Learning for Payload Data Processing: Hardware Performance Comparison
The path towards a multi-planetary species passes through the implementation of disruptive technological innovation. Artificial Intelligence and autonomy on spacecraft will be a fundamental part of this future. Hence, leveraging on-the-edge AI accelerators, such as FPGAs, GPUs, VPUs, ASICs, will constitute an essential component of the spacecraft hardware of tomorrow. This work presents a comparative work, specifically targeted to the use of on-board satellites. The tested platforms are Intel Myriad X, Nvidia Jetson Nano, and CPU (x64 architecture)
Authors
- Lorenzo Feruglio (ORCID: https://orcid.org/0000-0002-8437-8371)
- Mattia Varile (ORCID: https://orcid.org/0000-0001-9833-2537)
- Christian Cardenio
Publication Details
- Journal
- Digital Commons - USU (Utah State University)
- Published
- 2025-08-21
- DOI
- https://doi.org/10.26077/a8t6-1t78
- Primary Topic
- Parallel Computing and Optimization Techniques
- Type
- article