公开(公告)号：US20230342415A1

公开(公告)日：2023-10-26

申请号：US18192548

申请日：2023-03-29

Applicant: Beijing Horizon Robotics Technology Research and Development Co., Ltd.

Inventor： Yinan ZHANG ,  Delin LI ,  Jianjun LI

IPC: G06F17/15

CPC classification number: G06F17/153

Abstract: Disclosed are a feature extraction method and apparatus for a three-dimensional feature map, a storage medium, and an electronic device. The method includes: determining an overlay parameter based on depth information of a three-dimensional feature map to be processed; decomposing the three-dimensional feature map into a plurality of target two-dimensional feature maps based on the depth information and the overlay parameter; performing two-dimensional convolution processing on each of the plurality of target two-dimensional feature maps to obtain a plurality of initial feature maps; and determining a target feature map corresponding to the three-dimensional feature map based on the plurality of initial feature maps.

公开(公告)号：US20230409886A1

公开(公告)日：2023-12-21

申请号：US18247408

申请日：2022-02-10

Applicant: Beijing Horizon Robotics Technology Research and Development Co., Ltd.

Inventor： Zhuoran ZHAO ,  Kai YU ,  Chang HUANG ,  Zhenjiang WANG ,  Jianjun LI ,  Delin LI ,  Yinan ZHANG

IPC: G06N3/0464

CPC classification number: G06N3/0464

Abstract: The present disclosure provides a method and apparatus for deconvolving feature data using convolution hardware. The method includes: reading a feature map and deconvolution kernel into on-chip memory, and padding zeroes to the feature map; determining convolution kernels based on the deconvolution kernel; removing a row and/or column of each convolution kernel whose elements all are invalid weights, to obtain an optimized convolution kernel, and removing a corresponding row and/or column in the zero-padded feature map to obtain an corresponding optimized feature map; convolving each optimized convolution kernel with corresponding optimized feature map using the multiply-add array, to obtain convolutional outputs; and interleaving and synthesizing the convolutional outputs to obtain an interleaving synthetic output including at least a deconvolutional output corresponding to the feature map and deconvolution kernel. The method reduces hardware complexity, chip area and power consumption, and many invalid operations, improving operating efficiency of convolution hardware.