公开(公告)号：US20230186487A1

公开(公告)日：2023-06-15

申请号：US17549768

申请日：2021-12-13

Applicant: QUALCOMM Incorporated

Inventor： Rajeswaran CHOCKALINGAPURAMRAVINDRAN ,  Kristopher URQUHART ,  Jamie Menjay LIN ,  Risheek GARREPALLI

IPC: G06T7/215

CPC classification number: G06T7/215 ,  G06T2207/10016 ,  G06T2207/20084

Abstract: A computer-implemented method includes receiving a first input. The first input is interpolated based on a first shift along a first dimension and a second shift along a second dimension. A first output is generated based on the interpolated first input. The first output corresponds to a vectorized bilinear shift of the first input for use in place of grid sampling algorithms.

公开(公告)号：US20240070441A1

公开(公告)日：2024-02-29

申请号：US18451726

申请日：2023-08-17

Applicant: QUALCOMM Incorporated

Inventor： Zichao YUE ,  Sean Patrick Claye FOX ,  Janarbek MATAI ,  Kristopher URQUHART

IPC: G06N3/0464 ,  G06N3/10

CPC classification number: G06N3/0464 ,  G06N3/10

Abstract: A method of operating a depth-wise separable convolutional (DSC) network on a DSC accelerator includes determining a difference between a first throughput associated with a depth-wise convolution (DWC) engine of the DSC accelerator and a second throughput associated with a point-wise convolution (PWC) engine of the DSC accelerator. The method also includes selectively activating, for each layer of the DSC network, each first processing elements (PEs) in one or more of a first set of columns of first PEs associated with the DWC engine and/or each second PE in one or more of a second set of columns associated with the PWC engine based on the difference between the first throughput and the second throughput. The method further includes processing, for each layer of the DSC network, an input via the DSC accelerator based on selectively activating each first PE and/or each second PE.

公开(公告)号：US20230316090A1

公开(公告)日：2023-10-05

申请号：US18153687

申请日：2023-01-12

Applicant: QUALCOMM Incorporated

Inventor： Avijit CHAKRABORTY ,  Prathamesh Kalyan MANDKE ,  Joseph Binamira SORIAGA ,  Kristopher URQUHART

IPC: G06N3/098 ,  G06F9/54

CPC classification number: G06N3/098 ,  G06F9/54

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for performing federated learning. One example method generally includes sending model update data to a server, generating training metadata using a trained local machine learning model and local validation data, and sending the training metadata to the server. The trained local machine learning model generally incorporates the model update data and global model data defining a global machine learning model, and the training metadata generally includes data bout the trained local machine learning model used to determine when to discontinue federated learning operations for training the global machine learning model. Another example method generally includes sending a global model to a federated learning client device and receiving training metadata from the federated learning client device.