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This paper benchmarks the performance trade-offs among fully onboard, cloud-based, and split-computing architectures for lightweight VLMs using SmolVLM-256M as a representative lightweight VLM and shows that no deployment strategy is universally optimal.
This article categorizes existing UAV inspection architectures, identifies their key system challenges and architectural requirements, and experimentally assesses the feasibility of semantic edge intelligence on NVIDIA Jetson UAV-class hardware using the COCO-Bridge dataset.
DRLM achieves up to 51% faster inference and 67% reduced queuing delays in edge environments, all while maintaining accuracy.
LMEdge slashes latency while ensuring accuracy and resource efficiency in LLM inference across diverse edge devices.
ClusterLess slashes workflow completion times by up to 40% and nearly doubles deadline satisfaction in federated edge environments, outperforming existing methods.
Serverless orchestration falls apart when you move it to space, but this paper proposes a new architecture to fix it.
