Experimental comparison of graph-based approximate nearest neighbor search algorithms on edge devices
This provides practical insights for deploying nearest neighbor search on edge devices, though it is incremental as it focuses on comparative evaluation rather than new algorithmic development.
The paper experimentally compares graph-based approximate nearest neighbor search algorithms on edge devices for real-time applications like smart cities and autonomous vehicles, finding performance variations in metrics such as insertion/deletion latency and power consumption.
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.