# Experimental comparison of graph-based approximate nearest neighbor search algorithms on edge devices

arXiv: https://arxiv.org/abs/2411.14006 · HTML: https://mlsanity.com/paper/2411.14006/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 21 Nov 2024.

## Builds on (most cited first, 4 of 4)

- [A Comprehensive Survey and Experimental Comparison of Graph-Based Approximate Nearest Neighbor Search](https://mlsanity.com/paper/2101.12631/) (2021)
- [Why do Nearest Neighbor Language Models Work?](https://mlsanity.com/paper/2301.02828/) (2023)
- [HD-Index: Pushing the Scalability-Accuracy Boundary for Approximate kNN Search in High-Dimensional Spaces](https://mlsanity.com/paper/1804.06829/) (2018)
- [Practice with Graph-based ANN Algorithms on Sparse Data: Chi-square Two-tower model, HNSW, Sign Cauchy Projections](https://mlsanity.com/paper/2306.07607/) (2023)

## Abstract (arXiv)

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.