SpANNS: Optimizing Approximate Nearest Neighbor Search for Sparse Vectors Using Near Memory Processing
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866915711455068160 |
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| author | Zhang, Tianqi Ponzina, Flavio Rosing, Tajana |
| author_facet | Zhang, Tianqi Ponzina, Flavio Rosing, Tajana |
| contents | Approximate Nearest Neighbor Search (ANNS) is a fundamental operation in vector databases, enabling efficient similarity search in high-dimensional spaces. While dense ANNS has been optimized using specialized hardware accelerators, sparse ANNS remains limited by CPU-based implementations, hindering scalability. This limitation is increasingly critical as hybrid retrieval systems, combining sparse and dense embeddings, become standard in Information Retrieval (IR) pipelines. We propose SpANNS, a near-memory processing architecture for sparse ANNS. SpANNS combines a hybrid inverted index with efficient query management and runtime optimizations. The architecture is built on a CXL Type-2 near-memory platform, where a specialized controller manages query parsing and cluster filtering, while compute-enabled DIMMs perform index traversal and distance computations close to the data. It achieves 15.2x to 21.6x faster execution over the state-of-the-art CPU baselines, offering scalable and efficient solutions for sparse vector search. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_03229 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SpANNS: Optimizing Approximate Nearest Neighbor Search for Sparse Vectors Using Near Memory Processing Zhang, Tianqi Ponzina, Flavio Rosing, Tajana Databases Hardware Architecture Approximate Nearest Neighbor Search (ANNS) is a fundamental operation in vector databases, enabling efficient similarity search in high-dimensional spaces. While dense ANNS has been optimized using specialized hardware accelerators, sparse ANNS remains limited by CPU-based implementations, hindering scalability. This limitation is increasingly critical as hybrid retrieval systems, combining sparse and dense embeddings, become standard in Information Retrieval (IR) pipelines. We propose SpANNS, a near-memory processing architecture for sparse ANNS. SpANNS combines a hybrid inverted index with efficient query management and runtime optimizations. The architecture is built on a CXL Type-2 near-memory platform, where a specialized controller manages query parsing and cluster filtering, while compute-enabled DIMMs perform index traversal and distance computations close to the data. It achieves 15.2x to 21.6x faster execution over the state-of-the-art CPU baselines, offering scalable and efficient solutions for sparse vector search. |
| title | SpANNS: Optimizing Approximate Nearest Neighbor Search for Sparse Vectors Using Near Memory Processing |
| topic | Databases Hardware Architecture |
| url | https://arxiv.org/abs/2601.03229 |