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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.
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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
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.
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公开(公告)号：US20230123941A1
公开(公告)日：2023-04-20
申请号：US18081376
申请日：2022-12-14
Applicant: Google LLC
Inventor： Xiang Wu , David Simcha , Daniel Holtmann-Rice , Sanjiv Kumar , Ananda Theertha Suresh , Ruiqi Guo , Xinnan Yu
Abstract: The present disclosure provides systems and methods that include or otherwise leverage use of a multiscale quantization model that is configured to provide a quantized dataset. In particular, the multiscale quantization model can receive and perform vector quantization of a first dataset. The multiscale quantization model can generate a residual dataset based at least in part on a result of the vector quantization. The multiscale quantization model can apply a rotation matrix to the residual dataset to generate a rotated residual dataset that includes a plurality of rotated residuals. The multiscale quantization model can perform reparameterization of each rotated residual in the rotated residual dataset into a direction component and a scale component. The multiscale quantization model can perform product quantization of the direction components of the plurality of rotated residuals, and perform scalar quantization of the scale components of the plurality of rotated residuals.
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Patent Agency Ranking
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.
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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.
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公开(公告)号：US11874866B2
公开(公告)日：2024-01-16
申请号：US18081376
申请日：2022-12-14
Applicant: Google LLC
Inventor： Xiang Wu , David Simcha , Daniel Holtmann-Rice , Sanjiv Kumar , Ananda Theertha Suresh , Ruiqi Guo , Xinnan Yu
CPC classification number: G06F16/3347 , G06F16/313 , G06F16/319 , G06N20/00
Abstract: The present disclosure provides systems and methods that include or otherwise leverage use of a multiscale quantization model that is configured to provide a quantized dataset. In particular, the multiscale quantization model can receive and perform vector quantization of a first dataset. The multiscale quantization model can generate a residual dataset based at least in part on a result of the vector quantization. The multiscale quantization model can apply a rotation matrix to the residual dataset to generate a rotated residual dataset that includes a plurality of rotated residuals. The multiscale quantization model can perform reparameterization of each rotated residual in the rotated residual dataset into a direction component and a scale component. The multiscale quantization model can perform product quantization of the direction components of the plurality of rotated residuals, and perform scalar quantization of the scale components of the plurality of rotated residuals.
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公开(公告)号：US11531695B2
公开(公告)日：2022-12-20
申请号：US16638802
申请日：2018-05-14
Applicant: Google LLC
Inventor： Xiang Wu , David Simcha , Daniel Holtmann-Rice , Sanjiv Kumar , Ananda Theertha Suresh , Ruiqi Guo , Xinnan Yu
Abstract: The present disclosure provides systems and methods that include or otherwise leverage use of a multiscale quantization model that is configured to provide a quantized dataset. In particular, the multiscale quantization model can receive and perform vector quantization of a first dataset. The multiscale quantization model can generate a residual dataset based at least in part on a result of the vector quantization. The multiscale quantization model can apply a rotation matrix to the residual dataset to generate a rotated residual dataset that includes a plurality of rotated residuals. The multiscale quantization model can perform reparameterization of each rotated residual in the rotated residual dataset into a direction component and a scale component. The multiscale quantization model can perform product quantization of the direction components of the plurality of rotated residuals, and perform scalar quantization of the scale components of the plurality of rotated residuals.
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公开(公告)号：US20200183964A1
公开(公告)日：2020-06-11
申请号：US16638802
申请日：2018-05-14
Applicant: Google LLC
Inventor： Xiang Wu , David Simcha , Daniel Holtmann-Rice , Sanjiv Kumar , Ananda Theertha Suresh , Ruiqi Guo , Xinnan Yu
Abstract: The present disclosure provides systems and methods that include or otherwise leverage use of a multiscale quantization model that is configured to provide a quantized dataset. In particular, the multiscale quantization model can receive and perform vector quantization of a first dataset. The multiscale quantization model can generate a residual dataset based at least in part on a result of the vector quantization. The multiscale quantization model can apply a rotation matrix to the residual dataset to generate a rotated residual dataset that includes a plurality of rotated residuals. The multiscale quantization model can perform reparameterization of each rotated residual in the rotated residual dataset into a direction component and a scale component. The multiscale quantization model can perform product quantization of the direction components of the plurality of rotated residuals, and perform scalar quantization of the scale components of the plurality of rotated residuals.
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