The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Mar. 14, 2023

Filed:

Jun. 26, 2020
Applicant:

Nxp Usa, Inc., Austin, TX (US);

Inventors:

Ryan Haoyun Wu, San Jose, CA (US);

Satish Ravindran, Sunnyvale, CA (US);

Adam Fuks, Sunnyvale, CA (US);

Assignee:

NXP USA, Inc., Austin, TX (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 20/50 (2022.01); G06N 3/04 (2023.01); G06N 3/08 (2023.01); G06V 10/82 (2022.01); G05D 1/02 (2020.01);
U.S. Cl.
CPC ...
G06V 20/50 (2022.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06V 10/82 (2022.01); G05D 1/0231 (2013.01); G05D 1/0257 (2013.01);
Abstract

An early fusion network is provided that reduces network load and enables easier design of specialized ASIC edge processors through performing a portion of convolutional neural network layers at distributed edge and data-network processors prior to transmitting data to a centralized processor for fully-connected/deconvolutional neural networking processing. Embodiments can provide convolution and downsampling layer processing in association with the digital signal processors associated with edge sensors. Once the raw data is reduced to smaller feature maps through the convolution-downsampling process, this reduced data is transmitted to a central processor for further processing such as regression, classification, and segmentation, along with feature combination of the data from the sensors. In some embodiments, feature combination can be distributed to gateway or switch nodes closer to the edge sensors, thereby further reducing the data transferred to the central node and reducing the amount of computation performed there.


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