HRNN combines a navigation graph, ranked KNN graph, and reverse-neighbor lists with proxy-based candidate generation and materialized kNN-radii to achieve up to 10x higher throughput for approximate RkNN on datasets up to 10M vectors.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
ACRONYM claims a CAM-accelerated platform for dynamic vector databases that delivers over 90% recall at 8 million queries per second using 32 MB memory and 2.56 uJ per query while supporting updates without stalling.
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HRNN: A Hybrid Graph Index for Approximate Reverse k-Nearest Neighbor Search on High-Dimensional Vectors
HRNN combines a navigation graph, ranked KNN graph, and reverse-neighbor lists with proxy-based candidate generation and materialized kNN-radii to achieve up to 10x higher throughput for approximate RkNN on datasets up to 10M vectors.
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ACRONYM: Accelerated Approximate Nearest Neighbor Search in Memory for Dynamic Vector Databases
ACRONYM claims a CAM-accelerated platform for dynamic vector databases that delivers over 90% recall at 8 million queries per second using 32 MB memory and 2.56 uJ per query while supporting updates without stalling.