面向HPC平台的可扩展室内定位的资源感知模型选择
Resource-Aware Model Selection for Scalable Indoor Localization on HPC Platforms
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中文总结 AI 辅助
针对大规模室内定位中模块化模型推理成本高的问题，提出资源感知框架，通过层级候选剪枝和轨迹感知剪枝，将模型评估次数从735次降至10次，削减98.6%执行量。
中文摘要 AI 辅助
大规模室内定位在校园、智能建筑、工厂和数字孪生基础设施中日益需要，这些场景中的无线条件、接入点部署和空间布局会随时间演变。此类系统必须准确、可扩展且可维护，允许在不重新训练单一整体模型的情况下添加新建筑、楼层、房间和服务区域。模块化学习式定位通过为建筑、楼层和细粒度空间区域分配独立模型来支持这一目标。然而，这种可扩展性带来了高性能推理挑战：每次查询可能需要在数百或数千个局部模型中进行选择和执行，使得穷举推理在计算、加速器内存驻留、模型加载和调度方面成本高昂。本文提出了一种面向高性能和分布式计算平台上基于WiFi指纹的室内定位的资源感知模块化推理框架。该框架将局部自编码器模型组织成建筑-楼层-地点层级，并将定位表述为在大型预训练模型集成上的模型选择。为在资源约束下降低推理成本，我们引入了两种轻量级执行剪枝策略：层级候选剪枝，执行从粗到细的模型选择；以及轨迹感知剪枝，利用用户移动中的时间局部性将推理限制在空间上合理的邻近模型。在真实世界数据集上的实验表明，所提框架在不牺牲定位质量的情况下实现了可扩展推理。与对735个地点模型的穷举评估相比，层级候选剪枝仅需67次模型评估，而轨迹感知剪枝将此数量减少至仅10次，将模型执行削减了98.6%。
英文摘要
Large-scale indoor localization is increasingly needed in campuses, smart buildings, factories, and digital-twin infrastructures, where wireless conditions, access-point deployments, and spatial layouts evolve over time. Such systems must be accurate, extendable, and maintainable, allowing new buildings, floors, rooms, and service areas to be added without retraining a monolithic model. Modular learning-based localization supports this goal by assigning independent models to buildings, floors, and fine-grained spatial regions. However, this extendability introduces a high-performance inference challenge: each query may require selecting and executing among hundreds or thousands of local models, making exhaustive inference costly in computation, accelerator memory residency, model loading, and scheduling. This paper presents a resource-aware modular inference framework for WiFi fingerprint-based indoor localization on high-performance and distributed computing platforms. The framework organizes local autoencoder models into a building-floor-spot hierarchy and formulates localization as model selection over a large pretrained model ensemble. To reduce inference cost under resource constraints, we introduce two lightweight execution-pruning strategies: Hierarchical Candidate Pruning, which performs coarse-to-fine model selection, and Trajectory-Aware Pruning, which uses temporal locality in user movement to restrict inference to spatially plausible neighboring models. Experiments on a real-world dataset demonstrate that the proposed framework delivers scalable inference without sacrificing localization quality. Compared with exhaustive evaluation over 735 spot models, Hierarchical Candidate Pruning requires only 67 model evaluations, while Trajectory-Aware Pruning reduces this number to just 10, cutting model executions by 98.6%.
发表机构
- RIKEN Center for Computational Science（理化学研究所计算科学中心）
- The University of Osaka（大阪大学）
- The American University in Cairo（开罗美国大学）
机构由 AI 辅助整理，请以论文原文为准。
