公开(公告)号：US12235845B2

公开(公告)日：2025-02-25

申请号：US18474907

申请日：2023-09-26

Applicant: Google LLC

Inventor： Philip Wenjie Sun ,  Ruiqi Guo ,  Sanjiv Kumar

IPC: G06F16/245 ,  G06F16/2453 ,  G06F16/95 ,  G06F16/953

Abstract: Example quantization-based approximate nearest neighbors (ANN) search methods and systems (e.g., search engines) are tuned to perform at the speed-recall pareto frontier. With a desired search cost or recall as input, embodiments employ Lagrangian-based methods to perform constrained optimization on theoretically-grounded search cost and recall models. The resulting tunings, when paired with the efficient quantization-based ANN implementation of the embodiments, exhibit excellent performance on standard benchmarks while requiring minimal tuning or configuration complexity.

公开(公告)号：US20240119052A1

公开(公告)日：2024-04-11

申请号：US18474907

申请日：2023-09-26

Applicant: Google LLC

Inventor： Philip Wenjie Sun ,  Ruiqi Guo ,  Sanjiv Kumar

IPC: G06F16/2453 ,  G06F16/953

CPC classification number: G06F16/24545 ,  G06F16/24549 ,  G06F16/953

Abstract: The disclosure is directed towards automatically tuning quantization-based approximate nearest neighbors (ANN) search methods and systems (e.g., search engines) to perform at the speed-recall pareto frontier. With a desired search cost or recall as input, the embodiments employ Lagrangian-based methods to perform constrained optimization on theoretically-grounded search cost and recall models. The resulting tunings, when paired with the efficient quantization-based ANN implementation of the embodiments, exhibit excellent performance on standard benchmarks while requiring minimal tuning or configuration complexity.

公开(公告)号：US20240054102A1

公开(公告)日：2024-02-15

申请号：US17886860

申请日：2022-08-12

Applicant: Google LLC

Inventor： Filip Pavetic ,  David Simcha ,  Alexander-Teodor Voicu ,  Felix Chern ,  Philip Wenjie Sun ,  Ruiqi Guo ,  Hanna Maria Pasula ,  Martin Ulrich Seiler

IPC: G06F16/13 ,  G06F3/06

CPC classification number: G06F16/13 ,  G06F3/0649 ,  G06F3/0611 ,  G06F3/0685

Abstract: Provided is a scalable and cost-efficient storage architecture for large-scale datasets, such as Internet-scale datasets that include very large numbers (e.g., billions) of data elements. More particularly, provided is a bifurcated storage architecture that includes a first data index stored by a first set of storage media and a second data index stored by a second set of storage media, where the first set of storage media has a lower latency than the second set of storage media.