# Vector and Line Quantization for Billion-scale Similarity Search on GPUs

## TL;DR

This paper introduces VLQ-ADC, a novel vector and line quantization-based hierarchical inverted index for billion-scale approximate nearest neighbor search on GPUs, achieving higher accuracy and speed with comparable memory use.

## Contribution

The paper proposes a new hierarchical inverted index structure using vector and line quantization, improving search efficiency and accuracy for billion-scale datasets on GPUs.

## Key findings

- VLQ-ADC outperforms state-of-the-art systems in accuracy and speed.
- The method maintains comparable memory consumption to existing approaches.
- Extensive evaluation on SIFT1B and DEEP1B datasets validates effectiveness.

## Abstract

Billion-scale high-dimensional approximate nearest neighbour (ANN) search has become an important problem for searching similar objects among the vast amount of images and videos available online. The existing ANN methods are usually characterized by their specific indexing structures, including the inverted index and the inverted multi-index structure. The inverted index structure is amenable to GPU-based implementations, and the state-of-the-art systems such as Faiss are able to exploit the massive parallelism offered by GPUs. However, the inverted index requires high memory overhead to index the dataset effectively. The inverted multi-index structure is difficult to implement for GPUs, and also ineffective in dealing with database with different data distributions. In this paper we propose a novel hierarchical inverted index structure generated by vector and line quantization methods. Our quantization method improves both search efficiency and accuracy, while maintaining comparable memory consumption. This is achieved by reducing search space and increasing the number of indexed regions. We introduce a new ANN search system, VLQ-ADC, that is based on the proposed inverted index, and perform extensive evaluation on two public billion-scale benchmark datasets SIFT1B and DEEP1B. Our evaluation shows that VLQ-ADC significantly outperforms the state-of-the-art GPU- and CPU-based systems in terms of both accuracy and search speed. The source code of VLQ-ADC is available at https://github.com/zjuchenwei/vector-line-quantization.