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在NSW上:加快在边缘设备上的高维向量搜索,使用GPU优化的NSW
Taeyoon Park1, Haena Lee1, Yedam Na1
1Department of Computer Science and Engineering, Konkuk University, Seoul 05029, Republic of Korea.
Sensors (Basel, Switzerland)
|October 29, 2025
概括
在使用GPU的边缘设备上,ON-NSW优化了层次导航小世界 (HNSW). 这使得嵌入式平台上的工业物联网 (IIoT) 应用程序的近邻搜索 (ANNS) 更快.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 边缘计算 边缘计算
背景情况:
- 工业物联网 (IIoT) 应用程序越来越多地使用向量嵌入用于人工智能驱动的任务.
- 有效的近似近邻搜索 (ANNS) 对这些IIoT工作负载至关重要.
- 现有的基于图形的ANNS方法,如HNSW,主要是为高端服务器设计的,而不是边缘设备.
研究的目的:
- 为边缘设备量身定制的GPU优化的HNSW设计.
- 提高ANNS在资源有限的嵌入式系统上的性能.
- 解决边缘计算环境中传统ANNS方法的局限性.
主要方法:
- 推出了ON-NSW,这是GPU优化的HNSW变体,用于边缘设备.
- 使用平面图形结构来最大限度地提高GPU并行性.
- 在NVIDIA Jetson Orin Nano的统一内存中优化了组件的放置.
- 实现了形级平行邻居探索和轻量级同步.
主要成果:
- 与NVIDIA Jetson上的原始HNSW相比,ON-NSW实现了高达1.44倍的吞吐量.
- 保持了与原始HNSW相比可比的召回率.
- 在嵌入式边缘平台上展示了高效和高通量向量搜索功能.
结论:
- 在边缘设备上进行高通量向量搜索,ON-NSW提供了一个有效的解决方案.
- 该GPU优化策略适用于IIoT中的现实世界高维数据集.
- 通过高效的ANNS,在嵌入式系统上实现先进的AI应用程序.
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