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)

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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
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article

On-Board Deep Learning for Payload Data Processing: Hardware Performance Comparison

Lorenzo Feruglio, Mattia Varile, Christian Cardenio
Digital Commons - USU (Utah State University)
Parallel Computing and Optimization Techniques
article

On-Board Deep Learning for Payload Data Processing: Hardware Performance Comparison

Lorenzo Feruglio, Mattia Varile, Christian Cardenio
article en

Abstract

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)

Digital Commons - USU (Utah State University)
Parallel Computing and Optimization Techniques
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