Efficient Approximate Nearest Neighbor Search on a Raspberry Pi

Approximate Nearest Neighbors (ANN) search is a core task in Information Retrieval. However, the high computational demands and reliance on expensive infrastructures limit broader contributions to ANN research. Enabling efficient and effective ANN search on low-resource devices would allow researchers in low-income countries to participate in the ANN community, thereby democratizing the field. Despite its potential, the IR literature offers little work on the feasibility of ANN search under resource constraints. In this proposal, we explore efficient solutions for large-scale ANN search on low-resource devices. We report a preliminary experimentation highlighting current limitations and outlining future challenges.

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Publication Details

Published
2025-07-13
DOI
https://doi.org/10.1145/3726302.3730268
Citations
1
Primary Topic
Advanced Image and Video Retrieval Techniques
Type
article
Field-Weighted Citation Impact
1.28

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article

Efficient Approximate Nearest Neighbor Search on a Raspberry Pi

Cosimo Rulli, Franco Maria Nardini, Rossano Venturini, Silvio Martinico
1 citations
Advanced Image and Video Retrieval Techniques
1.28
article

Efficient Approximate Nearest Neighbor Search on a Raspberry Pi

Cosimo Rulli, Franco Maria Nardini, Rossano Venturini, Silvio Martinico
article en
1 citations

Abstract

Approximate Nearest Neighbors (ANN) search is a core task in Information Retrieval. However, the high computational demands and reliance on expensive infrastructures limit broader contributions to ANN research. Enabling efficient and effective ANN search on low-resource devices would allow researchers in low-income countries to participate in the ANN community, thereby democratizing the field. Despite its potential, the IR literature offers little work on the feasibility of ANN search under resource constraints. In this proposal, we explore efficient solutions for large-scale ANN search on low-resource devices. We report a preliminary experimentation highlighting current limitations and outlining future challenges.

University of Pisa (IT), Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo" (IT)
European Commission, HORIZON EUROPE Framework Programme
Openalex Percentile: Top 18%
Advanced Image and Video Retrieval Techniques
1.28
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