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:
Oct. 18, 2022

Filed:

Apr. 13, 2018
Applicant:

Microsoft Technology Licensing, Llc, Redmond, WA (US);

Inventors:

Amol Ashok Ambardekar, Redmond, WA (US);

Boris Bobrov, Kirkland, WA (US);

Chad Balling McBride, North Bend, WA (US);

George Petre, Redmond, WA (US);

Kent D. Cedola, Bellevue, WA (US);

Larry Marvin Wall, Seattle, WA (US);

Assignee:
Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/04 (2006.01); G06N 3/063 (2006.01); G06N 3/08 (2006.01); H03M 7/30 (2006.01); G06F 12/0862 (2016.01); G06F 12/10 (2016.01); G06F 3/06 (2006.01); G06F 9/38 (2018.01); G06N 3/10 (2006.01); G06F 9/46 (2006.01); G06F 1/324 (2019.01); G06F 12/08 (2016.01); G06F 15/80 (2006.01); G06F 17/15 (2006.01); G06N 3/06 (2006.01); H04L 45/02 (2022.01); H04L 67/02 (2022.01); G06F 9/30 (2018.01); H04L 67/1001 (2022.01); G06F 9/48 (2006.01); G06F 12/02 (2006.01); G06F 13/16 (2006.01); G06F 1/3234 (2019.01); G06F 13/28 (2006.01); H03M 7/46 (2006.01); H04L 45/50 (2022.01);
U.S. Cl.
CPC ...
H03M 7/3059 (2013.01); G06F 1/324 (2013.01); G06F 1/3275 (2013.01); G06F 3/0604 (2013.01); G06F 3/067 (2013.01); G06F 3/0631 (2013.01); G06F 9/30087 (2013.01); G06F 9/3836 (2013.01); G06F 9/3887 (2013.01); G06F 9/46 (2013.01); G06F 9/4881 (2013.01); G06F 12/0207 (2013.01); G06F 12/0238 (2013.01); G06F 12/08 (2013.01); G06F 12/0862 (2013.01); G06F 12/10 (2013.01); G06F 13/1673 (2013.01); G06F 13/1689 (2013.01); G06F 13/28 (2013.01); G06F 15/8007 (2013.01); G06F 17/15 (2013.01); G06N 3/04 (2013.01); G06N 3/049 (2013.01); G06N 3/0454 (2013.01); G06N 3/06 (2013.01); G06N 3/063 (2013.01); G06N 3/0635 (2013.01); G06N 3/08 (2013.01); G06N 3/10 (2013.01); H03M 7/6005 (2013.01); H03M 7/6011 (2013.01); H03M 7/70 (2013.01); H04L 45/04 (2013.01); H04L 67/02 (2013.01); H04L 67/1001 (2022.05); G06F 2209/484 (2013.01); G06F 2209/485 (2013.01); G06F 2212/657 (2013.01); H03M 7/46 (2013.01); H04L 45/50 (2013.01); Y02D 10/00 (2018.01);
Abstract

A deep neural network (DNN) module is disclosed that can dynamically partition neuron workload to reduce power consumption. The DNN module includes neurons and a group partitioner and scheduler unit. The group partitioner and scheduler unit divides a workload for the neurons into partitions in order to maximize the number of neurons that can simultaneously process the workload. The group partitioner and scheduler unit then assigns a group of neurons to each of the partitions. The groups of neurons in the DNN module process the workload in their assigned partition to generate a partial output value. The neurons in each group can then sum their partial output values to generate a final output value for the workload. The neurons can be powered down once the groups of neurons have completed processing their assigned workload to reduce power consumption.


Find Patent Forward Citations

Loading…