Efficient Nearest Neighbor (NN) search in high-dimensional spaces is a
foundation of many multimedia retrieval systems. Because it offers low
responses times, Product Quantization (PQ) is a popular solution. PQ compresses
high-dimensional vectors into short codes using several sub-quantizers, which
enables in-RAM storage of large databases. This allows fast answers to NN
queries, without accessing the SSD or HDD. The key feature of PQ is that it can
compute distances between short codes and high-dimensional vectors using
cache-resident lookup tables. The efficiency of this technique, named
Asymmetric Distance Computation (ADC), remains limited because it performs many
cache accesses.
In this paper, we introduce Quick ADC, a novel technique that achieves a 3 to
6 times speedup over ADC by exploiting Single Instruction Multiple Data (SIMD)
units available in current CPUs. Efficiently exploiting SIMD requires
algorithmic changes to the ADC procedure. Namely, Quick ADC relies on two key
modifications of ADC: (i) the use 4-bit sub-quantizers instead of the standard
8-bit sub-quantizers and (ii) the quantization of floating-point distances.
This allows Quick ADC to exceed the performance of state-of-the-art systems,
e.g., it achieves a Recall@100 of 0.94 in 3.4 ms on 1 billion SIFT descriptors
(128-bit codes).

本文提出了一种名为 Quick ADC 的新技术，通过利用当前 CPU 中可用的单指令多数据 (SIMD) 单元，实现了对 ADC 的 3-6 倍加速，并提出了两个关键算法修改，将 8 位子量化器替换为 4 位子量化器和定量化浮点距离。