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. 03, 2022

Filed:

Oct. 29, 2020
Applicant:

Intel Corporation, Santa Clara, CA (US);

Inventors:

Naveen Mellempudi, Bangalore, IN;

Dheevatsa Mudigere, Bangalore, IN;

Dipankar Das, Pune, IN;

Srinivas Sridharan, Bangalore, IN;

Assignee:

Intel Corporation, Santa Clara, CA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06T 1/20 (2006.01); G06N 3/063 (2006.01); G06F 17/16 (2006.01); G06F 7/523 (2006.01); G06F 5/01 (2006.01); G06F 7/501 (2006.01); G06F 17/15 (2006.01); G06N 3/04 (2006.01); G06F 7/544 (2006.01); G06N 3/08 (2006.01);
U.S. Cl.
CPC ...
G06T 1/20 (2013.01); G06F 5/01 (2013.01); G06F 7/501 (2013.01); G06F 7/523 (2013.01); G06F 7/5443 (2013.01); G06F 17/153 (2013.01); G06F 17/16 (2013.01); G06N 3/0445 (2013.01); G06N 3/0454 (2013.01); G06N 3/063 (2013.01); G06N 3/084 (2013.01); G06F 2207/382 (2013.01); G06F 2207/4824 (2013.01);
Abstract

One embodiment provides for a graphics processing unit to perform computations associated with a neural network, the graphics processing unit comprising compute unit including a hardware logic unit having dynamic precision fixed-point logic, the compute unit to receive a set of dynamic fixed-point tensors, compute, via the dynamic precision fixed-point logic, a right-shift value using an absolute maximum value within the set of dynamic fixed-point tensors and a dynamic range of the set of dynamic fixed-point tensors, right-shift data values within the set of dynamic fixed-point tensors based on the right-shift value, increment a shared exponent associated with the set of dynamic fixed-point tensors based on the right-shift value, perform a compute operation on the set of dynamic fixed-point tensors, and generate an output tensor via the compute operation on the set of dynamic fixed-point tensors.


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