Empirical benchmark of FAISS (main memory) versus FENSHSES (secondary memory) on Hamming-space nearest-neighbor search across indexing speed, latency, and RAM.
Fast and Exact Nearest Neighbor Search in Hamming Space on Full-Text Search Engines
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abstract
A growing interest has been witnessed recently from both academia and industry in building nearest neighbor search (NNS) solutions on top of full-text search engines. Compared with other NNS systems, such solutions are capable of effectively reducing main memory consumption, coherently supporting multi-model search and being immediately ready for production deployment. In this paper, we continue the journey to explore specifically how to empower full-text search engines with fast and exact NNS in Hamming space (i.e., the set of binary codes). By revisiting three techniques (bit operation, subs-code filtering and data preprocessing with permutation) in information retrieval literature, we develop a novel engineering solution for full-text search engines to efficiently accomplish this special but important NNS task. In the experiment, we show that our proposed approach enables full-text search engines to achieve significant speed-ups over its state-of-the-art term match approach for NNS within binary codes.
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cs.LG 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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An Empirical Comparison of FAISS and FENSHSES for Nearest Neighbor Search in Hamming Space
Empirical benchmark of FAISS (main memory) versus FENSHSES (secondary memory) on Hamming-space nearest-neighbor search across indexing speed, latency, and RAM.