Resuming Vision-Language Inference across Onboard Interruptions
We present OrbitWeaver, a framework that preserves VLM inference progress across onboard interruptions through coordinated checkpointing and recovery.
ProofPaper ↗
Key points
- Executing vision-language models (VLMs) aboard low-Earth orbit satellites enables flexible processing of observation data without first downlinking it to the ground.
- On commercial off-the-shelf payloads, however, a request may span multiple execution windows: power and thermal constraints, fault-triggered reboots, and competing payload demands can interrupt inference before it completes.
- Its phase-aware checkpointing selects recovery boundaries according to the state lifetimes and computational characteristics of vision encoding, language-model prefill, and decoding, balancing storage traffic against recomputation.
- Its dependency-aware dynamic recovery schedules restoration from partially completed states across the layer and token-chunk dimensions.
Sources (1)
- [1]Resuming Vision-Language Inference across Onboard InterruptionsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 04:14 AM
We present OrbitWeaver, a framework that preserves VLM inference progress across onboard interruptions through coordinated checkpointing and recovery.
Executing vision-language models (VLMs) aboard low-Earth orbit satellites enables flexible processing of observation data without first downlinking it to the ground.
Extractive summary: sentences quoted from the sources.
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