公开(公告)号：US20250068891A1

公开(公告)日：2025-02-27

申请号：US18724510

申请日：2022-02-18

Applicant: Intel Corporation

Inventor： Dongqi CAI ,  Anbang YAO ,  Chao LI ,  Yurong CHEN ,  Wenjian SHAO

IPC: G06N3/0464

Abstract: Methods, apparatus, systems and articles of manufacture (e.g., physical storage media) to implement dynamic triplet convolution for convolutional neural networks are disclosed. An example apparatus disclosed herein for a convolutional neural network is to calculate one or more scalar kernels based on an input feature map applied to a layer of the convolutional neural network, ones of the one or more scalar kernels corresponding to respective dimensions of a static multidimensional convolutional filter associated with the layer of the convolutional neural network. The disclosed example apparatus is also to scale elements of the static multidimensional convolutional filter along a first one of the dimensions based on a first one of the one or more scalar kernels corresponding to the first one of the dimensions to determine a dynamic multidimensional convolutional filter associated with the layer of the convolutional neural network.

公开(公告)号：US20250148761A1

公开(公告)日：2025-05-08

申请号：US18717894

申请日：2022-03-03

Applicant: Intel Corporation

Inventor： Dongqi CAI ,  Anbang YAO ,  Chao LI ,  Shandong WANG ,  Yurong CHEN

IPC: G06V10/771 ,  G06T5/20 ,  G06V10/40 ,  G06V20/64

Abstract: The disclosure provides an apparatus, method, device and medium for 3D dynamic sparse convolution. The method includes: receiving an input feature map of a 3D data sample; performing input feature map partition to divide the input feature map into a plurality of disjoint input feature map groups; performing a shared 3D dynamic sparse convolution to the plurality of disjoint input feature map groups respectively to obtain a plurality of output feature maps corresponding to the plurality of disjoint input feature map groups, wherein the shared 3D dynamic sparse convolution comprises a shared 3D dynamic sparse convolutional kernel; and performing output feature map grouping to sequentially stack the plurality of output feature maps to obtain an output feature map corresponding to the input feature map. (FIG. 2).

公开(公告)号：US20240312196A1

公开(公告)日：2024-09-19

申请号：US18565967

申请日：2021-11-30

Applicant: Intel Corporation

Inventor： Dongqi CAI ,  Anbang YAO ,  Yurong CHEN ,  Chao LI

IPC: G06V10/82 ,  G06N3/0464 ,  G06V20/40

CPC classification number: G06V10/82 ,  G06N3/0464 ,  G06V20/42

Abstract: An apparatus, method, device and medium for dynamic quadruple convolution in a 3-dimensional (3D) convolutional neural network (CNN) are provided. The method includes: a multi-dimensional attention block configured to: receive an input feature map of a video data sample; and dynamically generate convolutional kernel scalars along four dimensions of a 3-dimensional convolution kernel space based on the input feature map, the four dimensions comprising an output channel number, an input channel number, a temporal size and a spatial size; and a convolution block configured to sequentially multiply the generated convolutional kernel scalars with a static 3D convolution kernel in a matrix-vector product way to obtain a dynamic kernel of dynamic quadruple convolution.