Exact Nearest-Neighbor Search on Energy-Efficient FPGA Devices

• URL: http://arxiv.org/abs/2510.16736v1
• Date: Sun, 19 Oct 2025 07:29:16 GMT
• Title: Exact Nearest-Neighbor Search on Energy-Efficient FPGA Devices
• Authors: Patrizio Dazzi, William Guglielmo, Franco Maria Nardini, Raffaele Perego, Salvatore Trani,
• Abstract summary: The paper proposes two different energy-efficient solutions adopting the same FPGA low-level configuration.<n>The first solution maximizes system throughput by processing the queries of a batch in parallel over a streamed dataset.<n>The second minimizes latency by processing each kNN incoming query in parallel over an in-memory dataset.
• Score: 10.725513609195
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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