On Storage Neural Network Augmented Approximate Nearest Neighbor Search

TL;DR

This paper introduces a neural network-augmented approach for storage-based approximate nearest neighbor search, optimizing cluster selection to reduce data fetches and improve recall in large-scale datasets.

Contribution

It proposes a novel neural network method for predicting relevant clusters, enhancing storage-based ANN efficiency over existing techniques.

Findings

Achieves 90% recall on SIFT1M dataset.

Reduces data fetched by 80% compared to SPANN.

Reduces data fetched by 58% compared to exhaustive k-means.

Abstract

Large-scale approximate nearest neighbor search (ANN) has been gaining attention along with the latest machine learning researches employing ANNs. If the data is too large to fit in memory, it is necessary to search for the most similar vectors to a given query vector from the data stored in storage devices, not from that in memory. The storage device such as NAND flash memory has larger capacity than the memory device such as DRAM, but they also have larger latency to read data. Therefore, ANN methods for storage require completely different approaches from conventional in-memory ANN methods. Since the approximation that the time required for search is determined only by the amount of data fetched from storage holds under reasonable assumptions, our goal is to minimize it while maximizing recall. For partitioning-based ANNs, vectors are partitioned into clusters in the index building phase. In the search phase, some of the clusters are chosen, the vectors in the chosen clusters are fetched from storage, and the nearest vector is retrieved from the fetched vectors. Thus, the key point is to accurately select the clusters containing the ground truth nearest neighbor vectors. We accomplish this by proposing a method to predict the correct clusters by means of a neural network that is gradually refined by alternating supervised learning and duplicated cluster assignment. Compared to state-of-the-art SPANN and an exhaustive method using k-means clustering and linear search, the proposed method achieves 90% recall on SIFT1M with 80% and 58% less data fetched from storage, respectively.