Approximate Nearest-Neighbor Search (ANNS) efficiently finds data items whose embeddings are close to that of a given query in a high-dimensional space, aiming to balance accuracy with speed. Used in recommendation systems, image and video retrieval, natural language processing, and retrieval-augmented generation (RAG), ANNS algorithms such as IVFPQ, HNSW graphs, Annoy, and MRPT utilize graph, tree, clustering, and quantization techniques to navigate large vector spaces. Despite this progress, ANNS systems spend up to 99\% of query time to compute distances in their final refinement phase. In this paper, we present PANORAMA, a machine learning-driven approach that tackles the ANNS verification bottleneck through data-adaptive learned orthogonal transforms that facilitate the accretive refinement of distance bounds. Such transforms compact over 90\% of signal energy into the first half of dimensions, enabling early candidate pruning with partial distance computations. We integrate PANORAMA into state-of-the-art ANNS methods, namely IVFPQ/Flat, HNSW, MRPT, and Annoy, without index modification, using level-major memory layouts, SIMD-vectorized partial distance computations, and cache-aware access patterns. Experiments across diverse datasets -- from image-based CIFAR-10 and GIST to modern embedding spaces including OpenAI's Ada 2 and Large 3 -- demonstrate that PANORAMA affords a 2--30$\times$ end-to-end speedup with no recall loss.

翻译：近似最近邻搜索（ANNS）能够高效地在高维空间中查找与给定查询嵌入向量相近的数据项，旨在平衡精度与速度。ANNS算法（如IVFPQ、HNSW图、Annoy和MRPT）通过图结构、树结构、聚类和量化技术来遍历大规模向量空间，广泛应用于推荐系统、图像视频检索、自然语言处理以及检索增强生成（RAG）等领域。尽管取得了这些进展，ANNS系统在最终精炼阶段仍需消耗高达99%的查询时间用于距离计算。本文提出PANORAMA——一种基于机器学习的方法，通过数据自适应的学习正交变换来解决ANNS验证瓶颈，该变换能够促进距离界限的累积式精炼。此类变换可将超过90%的信号能量压缩至前一半维度，从而通过部分距离计算实现早期候选剪枝。我们将PANORAMA集成到最先进的ANNS方法（即IVFPQ/Flat、HNSW、MRPT和Annoy）中，无需修改索引结构，采用层级主序内存布局、SIMD向量化的部分距离计算以及缓存感知访问模式。在多样化数据集（从基于图像的CIFAR-10和GIST到现代嵌入空间，包括OpenAI的Ada 2和Large 3）上的实验表明，PANORAMA可在保持召回率不变的前提下实现2-30倍的端到端加速。