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Albireo is presented, a detector-agnostic, codec-free adaptive inference framework that wraps off-the-shelf detectors and decides when detector invocation can be safely skipped based on scene content and per-object temporal state, requiring no detector modification or retraining.
This paper proposes a vision for telemetry-informed adaptive compression in edge RAG, grounded in experimental evidence, and argues for runtime policies that dynamically manage compression, guided by workload features and edge telemetry.
We present RAGMark, a modular benchmarking framework for advanced Retrieval-Augmented Generation (RAG) systems targeting small-scale multi-GPU environments. RAGMark evaluates diverse RAG components, including retrievers, vector databases, prompt-processing methods, and generator models, while collecting detailed per-st...
Hydra is presented, a common-schema, phase-aware workload characterization framework for LLM inference on edge SoCs that enables reproducible, phase-aware characterization of edge LLM inference.
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