公开(公告)号：US20230222335A1

公开(公告)日：2023-07-13

申请号：US17997400

申请日：2021-06-11

Applicant: QUALCOMM Incorporated

Inventor： Hossein HOSSEINI ,  Christos LOUIZOS ,  Joseph Binamira SORIAGA

IPC: G06N3/08 ,  G06F21/31

CPC classification number: G06N3/08 ,  G06F21/31

Abstract: Certain aspects of the present disclosure provide techniques for authenticating a user based on a machine learning model, including receiving user authentication data associated with a user; generating output from a neural network model based on the user authentication data; determining a distance between the output and an embedding vector associated with the user; comparing the determined distance to a distance threshold; and making an authentication decision based on the comparison.

公开(公告)号：US20190354842A1

公开(公告)日：2019-11-21

申请号：US16413535

申请日：2019-05-15

Applicant: QUALCOMM Incorporated

Inventor： Christos LOUIZOS ,  Matthias REISSER ,  Tijmen Pieter Frederik BLANKEVOORT ,  Max WELLING

IPC: G06N3/04 ,  G06F17/18 ,  G06N3/08

Abstract: A method for quantizing a neural network includes modeling noise of parameters of the neural network. The method also includes assigning grid values to each realization of the parameters according to a concrete distribution that depends on a local fixed-point quantization grid and the modeled noise and. The method further includes computing a fixed-point value representing parameters of a hard fixed-point quantized neural network.

公开(公告)号：US20230076290A1

公开(公告)日：2023-03-09

申请号：US17792975

申请日：2021-02-04

Applicant: QUALCOMM Incorporated

Inventor： Rana Ali AMJAD ,  Markus NAGEL ,  Tijmen Pieter Frederik BLANKEVOORT ,  Marinus Willem VAN BAALEN ,  Christos LOUIZOS

IPC: G06N3/04

Abstract: A method for quantizing a pre-trained neural network includes computing a loss on a training set of candidate weights of the neural network. A rounding parameter is assigned to each candidate weight. The rounding parameter is a binary random value or a multinomial value. A quantized weight value is computed based on the loss and the rounding parameter.

公开(公告)号：US20210089922A1

公开(公告)日：2021-03-25

申请号：US17030315

申请日：2020-09-23

Applicant: QUALCOMM Incorporated

Inventor： Yadong LU ,  Ying WANG ,  Tijmen Pieter Frederik BLANKEVOORT ,  Christos LOUIZOS ,  Matthias REISSER ,  Jilei HOU

IPC: G06N3/08 ,  G06N3/063 ,  G06N3/04

Abstract: A method for compressing a deep neural network includes determining a pruning ratio for a channel and a mixed-precision quantization bit-width based on an operational budget of a device implementing the deep neural network. The method further includes quantizing a weight parameter of the deep neural network and/or an activation parameter of the deep neural network based on the quantization bit-width. The method also includes pruning the channel of the deep neural network based on the pruning ratio.

公开(公告)号：US20190354865A1

公开(公告)日：2019-11-21

申请号：US16417430

申请日：2019-05-20

Applicant: QUALCOMM Incorporated

Inventor： Matthias REISSER ,  Max WELLING ,  Efstratios GAVVES ,  Christos LOUIZOS

IPC: G06N3/08 ,  G06K9/62

Abstract: A neural network may be configured to receive, during a training phase of the neural network, a first input at an input layer of the neural network. The neural network may determine, during the training phase, a first classification at an output layer of the neural network based on the first input. The neural network may adjust, during the training phase and based on a comparison between the determined first classification and an expected classification of the first input, weights for artificial neurons of the neural network based on a loss function. The neural network may output, during an operational phase of the neural network, a second classification determined based on a second input, the second classification being determined by processing the second input through the artificial neurons using the adjusted weights.

公开(公告)号：US20240195434A1

公开(公告)日：2024-06-13

申请号：US18556622

申请日：2022-05-31

Applicant: QUALCOMM Incorporated

Inventor： Matthias REISSER ,  Aleksei TRIASTCYN ,  Christos LOUIZOS

IPC: H03M7/30 ,  G06F30/27 ,  G06N7/01

CPC classification number: H03M7/70 ,  G06F30/27 ,  G06N7/01 ,  H03M7/3057

Abstract: Certain aspects of the present disclosure provide techniques for performing federated learning, including receiving a global model from a federated learning server; determining an updated model based on the global model and local data; and sending the updated model to the federated learning server using relative entropy coding.

公开(公告)号：US20230169350A1

公开(公告)日：2023-06-01

申请号：US18040111

申请日：2021-09-28

Applicant: QUALCOMM Incorporated

Inventor： Christos LOUIZOS ,  Hossein HOSSEINI ,  Matthias REISSER ,  Max WELLING ,  Joseph Binamira SORIAGA

IPC: G06N3/098

CPC classification number: G06N3/098

Abstract: Aspects described herein provide techniques for performing federated learning of a machine learning model, comprising: for each respective client of a plurality of clients and for each training round in a plurality of training rounds: generating a subset of model elements for the respective client based on sampling a gate probability distribution for each model element of a set of model elements for a global machine learning model; transmitting to the respective client: the subset of model elements; and a set of gate probabilities based on the sampling, wherein each gate probability of the set of gate probabilities is associated with one model element of the subset of model elements; receiving from each respective client of the plurality of clients a respective set of model updates; and updating the global machine learning model based on the respective set of model updates from each respective client of the plurality of clients.

公开(公告)号：US20230058159A1

公开(公告)日：2023-02-23

申请号：US17759725

申请日：2021-04-29

Applicant: QUALCOMM Incorporated

Inventor： Marinus Willem VAN BAALEN ,  Christos LOUIZOS ,  Markus NAGEL ,  Tijmen Pieter Frederik BLANKEVOORT ,  Rana Ali AMJAD

IPC: G06N3/08

Abstract: Various embodiments include methods and devices for joint mixed-precision quantization and structured pruning. Embodiments may include determining whether a plurality of gates of quantization and pruning gates are selected for combination, and in response to determining that the plurality of gates are selected for combination, iteratively for each successive gate of the plurality of gates selected for combination quantizing a residual error of a quantized tensor to a scale of a next bit-width producing a residual error quantized tensor in which the next bit-width increases for each successive iteration, and adding the quantized tensor and the residual error quantized tensor producing a next quantized tensor in which the next quantized tensor has the next bit-width, and in which the next quantized tensor is the quantized tensor for a successive iteration.

公开(公告)号：US20220318412A1

公开(公告)日：2022-10-06

申请号：US17223946

申请日：2021-04-06

Applicant: QUALCOMM Incorporated

Inventor： Yunhui GUO ,  Hossein HOSSEINI ,  Christos LOUIZOS ,  Joseph Binamira SORIAGA

IPC: G06F21/62 ,  G06N3/08 ,  G06F17/18

Abstract: Certain aspects of the present disclosure provide techniques for improved machine learning using private variational dropout. A set of parameters of a global machine learning model is updated based on a local data set, and the set of parameters is pruned based on pruning criteria. A noise-augmented set of gradients is computed for a subset of parameters remaining after the pruning, based in part on a noise value, and the noise-augmented set of gradients is transmitted to a global model server.