NDANN: efficient SSD-based approximate nearest neighbor search through navigation

Kaixiang Yang; Hongya Wang; Zongyuan Tan; Jie Zhang

doi:10.1117/12.2652299

10 November 2022 NDANN: efficient SSD-based approximate nearest neighbor search through navigation

Kaixiang Yang, Hongya Wang, Zongyuan Tan, Jie Zhang

Proceedings Volume 12331, International Conference on Mechanisms and Robotics (ICMAR 2022); 123313K (2022) https://doi.org/10.1117/12.2652299
Event: International Conference on Mechanisms and Robotics (ICMAR 2022), 2022, Zhuhai, China

Abstract

Graph-based Approximate Nearest Neighbor Search (ANNS) algorithms have attracted more attention due to their better performance. Numerous ANNS optimization methods have been proposed by researchers. However, Current graph-based ANNS algorithms still cannot index billion-scale datasets on a single server with 256GB RAM. Although several researchers have investigated on this problem, we believe there is still an improvement in reducing disk accesses. In this paper, we provide a fast navigation layer to decrease the number of disk accesses by assisting query points in swiftly reaching the range of strongly connected components. Compared with the state-of-the-art ANNS algorithms, NDANN reduces the mean latency by about 20% under the same recall.

Citation Download Citation

Kaixiang Yang, Hongya Wang, Zongyuan Tan, and Jie Zhang "NDANN: efficient SSD-based approximate nearest neighbor search through navigation", Proc. SPIE 12331, International Conference on Mechanisms and Robotics (ICMAR 2022), 123313K (10 November 2022); https://doi.org/10.1117/12.2652299

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