---
title: "STLT: A Novel System for Energy-Aware Distributed Inference in the Edge-Cloud-Space Continuum"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2611.01233v1"
permalink: "https://feed.astralor.com/items/itm_ea7ac1489d6edd34d413bef3"
published: "2026-10-31T19:59:34.000Z"
score: 57
channel: "tech"
tags: ["推理效率","RAG","STLT","Novel System","Energy-Aware Distributed Inference","Edge-Cloud-Space Continuum Low","Earth Orbit","Earth"]
---

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

## At a glance

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 …

## Key points

- 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.

## Full text

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.

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