发表机构
University of Utah（犹他大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种数据并行硬件/软件机制，通过轻量级原语在内存中执行大规模近似暴力相似性搜索，并将剪枝作为后处理步骤，实现近乎完美召回率及数量级性能与能耗提升。
AI 中文摘要
高维向量数据集中的近似最近邻搜索（ANNS）是各种人工智能应用中普遍存在的重要操作。然而，由于维度灾难，此类操作在大型工作集规模下受到显著的带宽限制。用于加速ANNS的传统索引依赖于搜索空间剪枝作为预处理步骤，以缓解此类带宽需求，但这种优化要么以带宽效率降低为代价，要么以搜索质量下降为代价，或两者兼而有之。本文提出了一种数据并行的硬件/软件机制，用于在内存中执行大规模相似性搜索。我们提出了一种新颖的算法，通过轻量级原语简化各种距离度量下相似性搜索的计算需求，以在整个向量空间上执行快速且近似的数据并行暴力搜索。我们进一步构建了一个能够执行所需操作以生成每个数据点距离度量的内存系统，该度量随后用于实现剪枝作为后处理步骤。我们为用户控制所提出的系统提供了充分的软件支持。通过将这种搜索空间剪枝作为后处理步骤启用，我们在代表性工作负载上实现了近乎完美的召回率，同时在百万级和十亿级规模的工作负载上，相较于最先进的算法方法，实现了数量级的性能和能耗提升。
英文摘要
Approximate Nearest-Neighbor Search (ANNS) in high dimensional vector datasets is an application of significant prevalence across different AI applications. However, such an operation is significantly bandwidth limited at large workingset sizes owing to the curse of dimensionality. Traditional indices used to accelerate ANNS rely on search-space pruning as a preprocessing step to alleviate such bandwidth requirement, but such optimization occurs either at the cost of increased bandwidth-inefficiency and/or degradation of search quality. This paper proposes a data-parallel hardware/software mechanism for performing large-scale similarity search in-memory. We propose a novel algorithm to simplify the computation requirement for similarity search across various distance metrics through lightweight primitives to perform a fast and approximate data-parallel brute-force search on the entire vector space. We further build a memory system capable of executing the required operations to generate a distance metric per datapoints, which is then used to enable pruning as a post-processing step. We offer adequate software support for user control over the proposed system. By enabling such search-space pruning as a post-processing step, we achieve near-perfect recall across representative workloads while achieving orders of magnitude performance and energy improvement over state-of-the-art algorithmic approaches on million and billion-scale workloads.
Comments14 pages, 10 figures, 3 Tables
