# HAVEN: High-Bandwidth Flash Augmented Vector Engine for Large-Scale Approximate Nearest-Neighbor Search Acceleration

arXiv: https://arxiv.org/abs/2603.01175 · HTML: https://mlsanity.com/paper/2603.01175/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation.
- Builds on 2 of its 22 references that are in the map.
- First posted on arXiv 1 Mar 2026.

## Placed in

- Retrieval-augmented generation / [RAG pipeline design and evaluation](https://mlsanity.com/rag/rag-pipeline-design-and-evaluation/)

## Builds on (most cited first, 2 of 2)

- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)

## Built on by (most cited first, 6 of 6)

- [FlashAccel: Leveraging High-Bandwidth Flash (HBF) for High-Throughput LLM Inference](https://mlsanity.com/paper/2607.10186/) (2026)
- [QUBO-Optimized Evidence Selection for Retrieval-Augmented Question Answering with Unconventional Solvers](https://mlsanity.com/paper/2607.12334/) (2026)
- [FLINT: Efficiently Leveraging High Bandwidth Flash for Capacity-Scalable LLM Inference Acceleration](https://mlsanity.com/paper/2608.25062/) (2026)
- [SPLASH: Co-Designing Sparse Attention with High-Bandwidth Flash for Efficient Long-Context Inference](https://mlsanity.com/paper/2609.23816/) (2026)
- [Hot-Cold Tiering of HBM and High Bandwidth Flash for Agentic LLM Serving](https://mlsanity.com/paper/2609.25782/) (2026)
- [Characterizing High Bandwidth Flash for LLM Serving](https://mlsanity.com/paper/2609.39131/) (2026)

## Abstract (arXiv)

Retrieval-Augmented Generation (RAG) relies on large-scale Approximate Nearest Neighbor Search (ANNS) to retrieve semantically relevant context for large language models. Among ANNS methods, IVF-PQ offers an attractive balance between memory efficiency and search accuracy. However, achieving high recall requires reranking which fetches full-precision vectors for reranking, and the billion-scale vector databases need to reside in CPU DRAM or SSD due to the limited capacity of GPU HBM. This off-GPU data movement introduces substantial latency and throughput degradation. We propose HAVEN, a GPU architecture augmented with High-Bandwidth Flash (HBF) which is a recently introduced die-stacked 3D NAND technology engineered to deliver terabyte-scale capacity and hundreds of GB/s read bandwidth. By integrating HBF and near-storage search unit as an on-package complement to HBM, HAVEN enables the full-precision vector database to reside entirely on-device, eliminating PCIe and DDR bottlenecks during reranking. Through detailed modeling of re-architected 3D NAND subarrays, power-constrained HBF bandwidth, and end-to-end IVF-PQ pipelines, we demonstrate that HAVEN improves reranking throughput by up to 20x and latency up to 40x across billion-scale datasets compared to GPU-DRAM and GPU-SSD systems. Our results show that HBF-augmented GPUs enable high-recall retrieval at throughput previously achievable only without reranking, offering a promising direction for memory-centric AI accelerators.