Exact Nearest-Neighbor Search on Energy-Efficient FPGA Devices
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915563052204032 |
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| author | Dazzi, Patrizio Guglielmo, William Nardini, Franco Maria Perego, Raffaele Trani, Salvatore |
| author_facet | Dazzi, Patrizio Guglielmo, William Nardini, Franco Maria Perego, Raffaele Trani, Salvatore |
| contents | This paper investigates the usage of FPGA devices for energy-efficient exact kNN search in high-dimension latent spaces. This work intercepts a relevant trend that tries to support the increasing popularity of learned representations based on neural encoder models by making their large-scale adoption greener and more inclusive. The paper proposes two different energy-efficient solutions adopting the same FPGA low-level configuration. The first solution maximizes system throughput by processing the queries of a batch in parallel over a streamed dataset not fitting into the FPGA memory. The second minimizes latency by processing each kNN incoming query in parallel over an in-memory dataset. Reproducible experiments on publicly available image and text datasets show that our solution outperforms state-of-the-art CPU-based competitors regarding throughput, latency, and energy consumption. Specifically, experiments show that the proposed FPGA solutions achieve the best throughput in terms of queries per second and the best-observed latency with scale-up factors of up to 16.6X. Similar considerations can be made regarding energy efficiency, where results show that our solutions can achieve up to 11.9X energy saving w.r.t. strong CPU-based competitors. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_16736 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Exact Nearest-Neighbor Search on Energy-Efficient FPGA Devices Dazzi, Patrizio Guglielmo, William Nardini, Franco Maria Perego, Raffaele Trani, Salvatore Information Retrieval Distributed, Parallel, and Cluster Computing This paper investigates the usage of FPGA devices for energy-efficient exact kNN search in high-dimension latent spaces. This work intercepts a relevant trend that tries to support the increasing popularity of learned representations based on neural encoder models by making their large-scale adoption greener and more inclusive. The paper proposes two different energy-efficient solutions adopting the same FPGA low-level configuration. The first solution maximizes system throughput by processing the queries of a batch in parallel over a streamed dataset not fitting into the FPGA memory. The second minimizes latency by processing each kNN incoming query in parallel over an in-memory dataset. Reproducible experiments on publicly available image and text datasets show that our solution outperforms state-of-the-art CPU-based competitors regarding throughput, latency, and energy consumption. Specifically, experiments show that the proposed FPGA solutions achieve the best throughput in terms of queries per second and the best-observed latency with scale-up factors of up to 16.6X. Similar considerations can be made regarding energy efficiency, where results show that our solutions can achieve up to 11.9X energy saving w.r.t. strong CPU-based competitors. |
| title | Exact Nearest-Neighbor Search on Energy-Efficient FPGA Devices |
| topic | Information Retrieval Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2510.16736 |