  Fast k-Nearest Neighbor search over real-valued vector spaces (KNN) is an
important algorithmic task for information retrieval and recommendation
systems. We present a method for using reduced precision to represent vectors
through quantized integer values, enabling both a reduction in the memory
overhead of indexing these vectors and faster distance computations at query
time. While most traditional quantization techniques focus on minimizing the
reconstruction error between a point and its uncompressed counterpart, we focus
instead on preserving the behavior of the underlying distance metric.
Furthermore, our quantization approach is applied at the implementation level
and can be combined with existing KNN algorithms. Our experiments on both open
source and proprietary datasets across multiple popular KNN frameworks validate
that quantized distance metrics can reduce memory by 60% and improve query
throughput by 30%, while incurring only a 2% reduction in recall.
