用FPGA实现高效精准近邻搜索，显著降低能耗与延迟。
Exact Nearest-Neighbor Search on Energy-Efficient FPGA Devices
- 基于FPGA并行处理批量查询或单个查询，适配不同内存场景。
- 相比主流CPU方案，吞吐量提升16.6倍，延迟最低，能效提升11.9倍。
- 适合需要高能效、低延迟的大型神经编码表征检索应用。
本文研究在能效友好的FPGA设备上实现高维潜在空间中的精确kNN搜索。针对神经编码模型生成的表征日益流行的趋势，提出两种基于相同FPGA底层配置的节能解决方案：第一种通过并行处理批量查询，在流式数据集（无法全部载入FPGA内存）上最大化系统吞吐量；第二种通过并行处理每个独立查询，在内存内数据集上最小化延迟。在公开图像和文本数据集上的可复现实验表明，所提方案在吞吐量、延迟和能效方面均优于当前最先进的基于CPU的方案。具体而言，该方案在查询每秒数（queries per second）上达到最优，延迟最优，且规模扩展因子最高达16.6X；在能效方面，相比强于主流的CPU方案，最高可节省11.9倍能耗。
原文摘要 · Abstract (English)
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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