AQR-HNSW: Accelerating Approximate Nearest Neighbor Search via Density-aware Quantization and Multi-stage Re-ranking

TL;DR

AQR-HNSW introduces a density-aware quantization and multi-stage re-ranking framework that significantly improves the scalability, speed, and memory efficiency of HNSW-based approximate nearest neighbor search in large-scale vector databases.

Contribution

It proposes a novel combination of adaptive quantization and multi-stage re-ranking to enhance HNSW scalability and efficiency, addressing memory and computation bottlenecks.

Findings

Achieves 2.5-3.3x higher QPS than existing HNSW methods.

Reduces memory usage by 75% for the index graph.

Speeds up index construction by 5x.

Abstract

Approximate Nearest Neighbor (ANN) search has become fundamental to modern AI infrastructure, powering recommendation systems, search engines, and large language models across industry leaders from Google to OpenAI. Hierarchical Navigable Small World (HNSW) graphs have emerged as the dominant ANN algorithm, widely adopted in production systems due to their superior recall versus latency balance. However, as vector databases scale to billions of embeddings, HNSW faces critical bottlenecks: memory consumption expands, distance computation overhead dominates query latency, and it suffers suboptimal performance on heterogeneous data distributions. This paper presents Adaptive Quantization and Rerank HNSW (AQR-HNSW), a novel framework that synergistically integrates three strategies to enhance HNSW scalability. AQR-HNSW introduces (1) density-aware adaptive quantization, achieving 4x compression while preserving distance relationships; (2) multi-state re-ranking that reduces unnecessary computations by 35%; and (3) quantization-optimized SIMD implementations delivering 16-64 operations per cycle across architectures. Evaluation on standard benchmarks demonstrates 2.5-3.3x higher queries per second (QPS) than state-of-the-art HNSW implementations while maintaining over 98% recall, with 75% memory reduction for the index graph and 5x faster index construction.