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:
May. 14, 2024

Filed:

Oct. 01, 2018
Applicant:

Expedera, Inc., Santa Clara, CA (US);

Inventors:

Sharad Vasantrao Chole, San Jose, CA (US);

Shang-Tse Chuang, Los Altos, CA (US);

Siyad Chih-Hua Ma, Palo Alto, CA (US);

Assignee:

Expedera, Inc., Santa Clara, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/04 (2006.01); G06F 9/345 (2018.01); G06F 9/38 (2018.01); G06F 17/16 (2006.01); G06N 3/08 (2006.01); G06N 3/084 (2023.01);
U.S. Cl.
CPC ...
G06N 3/04 (2013.01); G06F 9/345 (2013.01); G06F 17/16 (2013.01); G06N 3/084 (2013.01); G06F 9/3877 (2013.01);
Abstract

Artificial intelligence is an increasingly important sector of the computer industry. However, artificial intelligence is very computationally intensive field. Fortunately, many of the required calculations can be performed in parallel such that specialized processors can greatly increase computation performance. In particular, Graphics Processor Units (GPUs) are often used in artificial intelligence. Although GPUs have helped, they are not ideal for artificial intelligence. Specifically, GPUs are used to compute matrix operations in one direction with a pipelined architecture. However, artificial intelligence is a field that uses both forward propagation computations and back propagation calculations. To efficiently perform artificial intelligence calculations, a symmetric matrix processing element is introduced. The symmetric matrix processing element can perform forward propagation and backward propagation calculations just as easily. Furthermore, both of these calculations can be performed without reloading weight matrix values.


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