Experimental Comparison Of Graph-based Approximate Nearest Neighbor Search Algorithms On Edge Devices

Abstract

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.

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyganbarov2024experimental

Related papers

• A Comprehensive Survey And Experimental Comparison Of Graph-based Approximate Nearest Neighbor Search (2021)17.35
• Search Optimization With Query Likelihood Boosting And Two-level Approximate Search For Edge Devices (2023)0.00
• A Theoretical Analysis Of Nearest Neighbor Search On Approximate Near Neighbor Graph (2023)0.00
• Parlayann: Scalable And Deterministic Parallel Graph-based Approximate Nearest Neighbor Search Algorithms (2023)10.35
• Approximate Nearest Neighbour Search On Dynamic Datasets: An Investigation (2024)0.00
• EFANNA : An Extremely Fast Approximate Nearest Neighbor Search Algorithm Based On Knn Graph (2016)0.00
• GGNN: Graph-based GPU Nearest Neighbor Search (2019)13.39
• Results Of The Neurips'21 Challenge On Billion-scale Approximate Nearest Neighbor Search (2022)0.00