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：US20230252658A1

公开(公告)日：2023-08-10

申请号：US17650027

申请日：2022-02-04

Applicant: QUALCOMM Incorporated

Inventor： Hong CAI ,  Shichong PENG ,  Janarbek MATAI ,  Jamie Menjay LIN ,  Debasmit DAS ,  Fatih Murat PORIKLI

IPC: G06T7/50 ,  G06T7/10 ,  G06N3/04

CPC classification number: G06T7/50 ,  G06T7/10 ,  G06N3/0454 ,  G06T2207/20084 ,  G06T2207/20212

Abstract: Certain aspects of the present disclosure provide techniques for generating fine depth maps for images of a scene based on semantic segmentation and segment-based refinement neural networks. An example method generally includes generating, through a segmentation neural network, a segmentation map based on an image of a scene. The segmentation map generally comprises a map segmenting the scene into a plurality of regions, and each region of the plurality of regions is generally associated with one of a plurality of categories. A first depth map of the scene is generated through a first depth neural network based on a depth measurement of the scene. A second depth map of the scene is generated through a depth refinement neural network based on the segmentation map and the first depth map. One or more actions are taken based on the second depth map of the scene.

公开(公告)号：US20230005165A1

公开(公告)日：2023-01-05

申请号：US17808520

申请日：2022-06-23

Applicant: QUALCOMM Incorporated

Inventor： Hong CAI ,  Janarbek MATAI ,  Shubhankar Mangesh BORSE ,  Yizhe ZHANG ,  Amin ANSARI ,  Fatih Murat PORIKLI

IPC: G06T7/50 ,  G06N3/08 ,  G06N3/04

Abstract: Certain aspects of the present disclosure provide techniques for cross-task distillation. A depth map is generated by processing an input image using a first machine learning model, and a segmentation map is generated by processing the depth map using a second machine learning model. A segmentation loss is computed based on the segmentation map and a ground-truth segmentation map, and the first machine learning model is refined based on the segmentation loss.