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:
Nov. 26, 2024

Filed:

Jan. 12, 2018
Applicant:

Intel Corporation, Santa Clara, CA (US);

Inventors:

Srinivas Sridharan, Bangalore, IN;

Dheevatsa Mudigere, Bangalore, IN;

Assignee:

Intel Corporation, Santa Clara, CA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06N 3/08 (2023.01); G06F 9/54 (2006.01); G06N 3/04 (2023.01); G06N 3/063 (2023.01); G06N 3/084 (2023.01); G06T 15/00 (2011.01); G06T 15/04 (2011.01); G06T 15/80 (2011.01); G06T 17/10 (2006.01); G06T 17/20 (2006.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); G06F 9/54 (2013.01); G06N 3/04 (2013.01); G06N 3/063 (2013.01); G06T 15/005 (2013.01); G06N 3/084 (2013.01); G06T 15/04 (2013.01); G06T 15/80 (2013.01); G06T 17/10 (2013.01); G06T 17/20 (2013.01);
Abstract

One embodiment provides for a system to configure distributed training of a neural network. The system includes memory to store a library to facilitate transmission of data during distributed training of the neural network; a network interface to transmit and receive gradient data associated with the trainable parameters; a general-purpose processor to execute instructions provided by the library, the instructions to cause the general-purpose processor to configure the network interface to transmit and receive the gradient data associated with the trainable parameters during a workflow of a machine learning framework; and a graphics processor to perform compute operations associated with machine learning framework workflow to generate the gradient data associated with the trainable parameters, wherein, based on the machine learning framework workflow, the library is to interleave the compute operations on the graphics processor with transmission and receipt of gradient data via the network interface.


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