This paper investigates how to substantially reduce the memory footprint of learned sparse retrieval indexes without sacrificing the efficiency of state-of-the-art retrieval data structures. Building on SEISMIC, we revisit both levels of its design: the inverted index used to select candidates and the forward index use...
Franco Maria Nardini, Luca Rizzo, Cosimo Rulli et al.· 0 citations
FastPair is presented, a GPU decoder that optimizes the existing dictionary decoding process by reorganizing lookups and assembling decoded substrings for contiguous output writes, reaching up to 1.6 TB/s on a B300.
J. Isaacs, Francesco Gargiulo, P. Boncz et al.· 0 citations
PUMA, a sparse autoencoder recipe that maps universal multimodal embeddings to compact sparse codes without retraining the backbone, reduces vector storage by 8-16x (FP32) and is up to 25x faster than exact dense scoring on larger candidate pools, enabling efficient multimodal retrieval.
Matteo Attimonelli, Alessandro De Bellis, Franco Maria Nardini et al.· 0 citations
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