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STLT: A Novel System for Energy-Aware Distributed Inference in the Edge-Cloud-Space Continuum — arXiv:2611.01233

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.01233
License (as archived): CC0-1.0
Archived: 2026-11-03T10:35:45.398Z
SHA-256 of the archived text: 193f518a5b3e5b64e1f588b6705c8f3fda267e90bb74095cef2e3844cb067848
Stable URL of this page: https://metraipolis.com/documento/dc_ba5545fefeee

How to cite

STLT: A Novel System for Energy-Aware Distributed Inference in the Edge-Cloud-Space Continuum — arXiv:2611.01233 — arXiv (en), https://arxiv.org/abs/2611.01233 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ba5545fefeee (Metraipolis archive dc_ba5545fefeee, sha256 193f518a5b3e5b64e1f588b6705c8f3fda267e90bb74095cef2e3844cb067848)

BibTeX

@misc{dcba5545fefeee,
  title = {STLT: A Novel System for Energy-Aware Distributed Inference in the Edge-Cloud-Space Continuum — arXiv:2611.01233},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.01233},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ba5545fefeee; sha256 193f518a5b3e5b64e1f588b6705c8f3fda267e90bb74095cef2e3844cb067848},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.01233. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Low Earth Orbit satellites are pivotal for Earth observation tasks such as wildfire detection, capturing imagery for analysis by Deep Neural Networks (DNNs). On-board analysis reduces downlink volume, but severe energy constraints force a trade-off between computation and data transmission. Splitting the DNN between satellite and ground turns this balance into a design choice, where the split point determines combined computation and downlink energy. Existing approaches typically rely on static split-point decisions that fail to account for dynamic space conditions, or on complex algorithms, historical data, or prediction models that introduce significant overhead. To address these shortcomings, we present STLT, a two-stage system for energy-aware distributed inference between LEO satellites and a ground station. First, an offline analyzer characterizes the DNN architecture to identify a set of favorable split points, consequently reducing the search space. Second, an on-board algorithm performs lightweight energy-aware split selection, dynamically choosing from this pre-computed set based on the current state of the environment. We evaluate STLT on a real-world satellite telemetry dataset, showing that it reduces the split-candidate search space by 30.7% on average, lowers decision latency by 25.1% and energy by 27.5% on average, and delivers up to 20.1% more inference tasks than a state-of-the-art satellite-ground co-inference baseline while consuming 8.8% less energy overall.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Distributed, Parallel, and Cluster Computing (cs.DC) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2611.01233 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T10:29:48.752Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).