对比多种图结构近似最近邻算法在边缘设备上的表现，为实时追踪系统选型提供依据。
Experimental comparison of graph-based approximate nearest neighbor search algorithms on edge devices
- 在边缘设备上实测多种图结构近似最近邻算法性能
- 评估包含插入删除延迟与功耗在内的多维度指标
- 适合关注边缘计算中近邻搜索效率的开发者和研究者
本文对部署在边缘设备上的多种图结构近似最近邻（ANN）搜索算法进行了实验对比，面向智慧城市基础设施和自动驾驶等实时近邻搜索应用场景。据我们所知，此类针对边缘设备的具体对比分析尚未开展。现有研究多局限于标准硬件上的单线程实现，而本研究充分利用边缘设备的完整计算与存储能力，引入向量插入/删除延迟及功耗等新指标。该综合评估旨在为基于近邻搜索的边缘实时追踪系统提供性能与适用性方面的关键洞见。
原文摘要 · Abstract (English)
In this paper, we present an experimental comparison of various graph-based approximate nearest neighbor (ANN) search algorithms deployed on edge devices for real-time nearest neighbor search applications, such as smart city infrastructure and autonomous vehicles. To the best of our knowledge, this specific comparative analysis has not been previously conducted. While existing research has explored graph-based ANN algorithms, it has often been limited to single-threaded implementations on standard commodity hardware. Our study leverages the full computational and storage capabilities of edge devices, incorporating additional metrics such as insertion and deletion latency of new vectors and power consumption. This comprehensive evaluation aims to provide valuable insights into the performance and suitability of these algorithms for edge-based real-time tracking systems enhanced by nearest-neighbor search algorithms.
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