Optimizing deep learning models for on-orbit deployment through neural architecture search.

ABSTRACT: Advancements in spaceborne edge computing have facilitated the incorporation of Artificial Intelligence (AI)-powered chips into CubeSats, enabling intelligent data handling and enhanced analytical capabilities with greater operational autonomy. This class of satellites faces stringent energy and memory constraints, necessitating lightweight models typically obtained via compression techniques. This paper addresses model compression through Neural Architecture Search (NAS), enabling computational efficiency and balancing accuracy, size, and latency. More specifically, we design an evolutionary-based NAS framework for onboard processing and evaluate it on both burned-area segmentation and classification tasks. The proposed solution jointly optimizes network architecture and deployment for ha

SUBMITTER: Del Prete R 

PROVIDER: S-EPMC12572123 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature