公开(公告)号：US20210064634A1

公开(公告)日：2021-03-04

申请号：US17001850

申请日：2020-08-25

Applicant: Google LLC

Inventor： Ruiqi Guo ,  David Simcha ,  Quan Geng ,  Felix Chern ,  Sanjiv Kumar ,  Xiang Wu

IPC: G06F16/25 ,  G06F16/2457 ,  H03M7/30

Abstract: Generally, the present disclosure is directed to systems and methods of quantizing a database with respect to a novel loss or quantization error function which applies a weight to an error measurement of quantized elements respectively corresponding to the datapoints in the database. The weight is determined based on the magnitude of an inner product between the respective datapoints and a query compared therewith. In contrast to previous work, embodiments of the proposed loss function are responsive to the expected magnitude of an inner product between the respective datapoints and a query compared therewith and can prioritize error reduction for higher-ranked pairings of the query and the datapoints. Thus, the systems and methods of the present disclosure provide solutions to some of the problems with traditional quantization approaches, which regard all error as equally impactful.

公开(公告)号：US20240061889A1

公开(公告)日：2024-02-22

申请号：US18456688

申请日：2023-08-28

Applicant: Google LLC

Inventor： Ruiqi Guo ,  David Simcha ,  Quan Geng ,  Felix Chern ,  Sanjiv Kumar ,  Xiang Wu

IPC: G06F16/906 ,  G06F16/2457 ,  G06F16/25 ,  H03M7/30

CPC classification number: G06F16/906 ,  G06F16/24578 ,  G06F16/258 ,  H03M7/30

Abstract: Generally, the present disclosure is directed to systems and methods of quantizing a database with respect to a novel loss or quantization error function which applies a weight to an error measurement of quantized elements respectively corresponding to the datapoints in the database. The weight is determined based on the magnitude of an inner product between the respective datapoints and a query compared therewith. In contrast to previous work, embodiments of the proposed loss function are responsive to the expected magnitude of an inner product between the respective datapoints and a query compared therewith and can prioritize error reduction for higher-ranked pairings of the query and the datapoints. Thus, the systems and methods of the present disclosure provide solutions to some of the problems with traditional quantization approaches, which regard all error as equally impactful.

公开(公告)号：US11775589B2

公开(公告)日：2023-10-03

申请号：US17001850

申请日：2020-08-25

Applicant: Google LLC

Inventor： Ruiqi Guo ,  David Simcha ,  Quan Geng ,  Felix Chern ,  Sanjiv Kumar ,  Xiang Wu

IPC: G06F16/20 ,  G06F16/906 ,  G06F16/25 ,  H03M7/30 ,  G06F16/2457

CPC classification number: G06F16/906 ,  G06F16/24578 ,  G06F16/258 ,  H03M7/30

Abstract: Generally, the present disclosure is directed to systems and methods of quantizing a database with respect to a novel loss or quantization error function which applies a weight to an error measurement of quantized elements respectively corresponding to the datapoints in the database. The weight is determined based on the magnitude of an inner product between the respective datapoints and a query compared therewith. In contrast to previous work, embodiments of the proposed loss function are responsive to the expected magnitude of an inner product between the respective datapoints and a query compared therewith and can prioritize error reduction for higher-ranked pairings of the query and the datapoints. Thus, the systems and methods of the present disclosure provide solutions to some of the problems with traditional quantization approaches, which regard all error as equally impactful.

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.