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:
Aug. 08, 2023

Filed:

Jan. 25, 2022
Applicant:

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

Inventors:

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

Larry Marvin Wall, Seattle, WA (US);

Boris Bobrov, Kirkland, WA (US);

George Petre, Redmond, WA (US);

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

Amol Ashok Ambardekar, Redmond, WA (US);

Assignee:
Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
H03M 7/30 (2006.01); G06N 3/04 (2023.01); G06N 3/063 (2023.01); G06F 12/0862 (2016.01); G06F 9/46 (2006.01); G06F 1/324 (2019.01); G06F 3/06 (2006.01); G06F 9/38 (2018.01); G06F 12/08 (2016.01); G06F 12/10 (2016.01); G06F 15/80 (2006.01); G06F 17/15 (2006.01); G06N 3/049 (2023.01); G06N 3/06 (2006.01); G06N 3/08 (2023.01); G06N 3/10 (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); G06N 3/045 (2023.01); G06N 3/065 (2023.01); 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 3/045 (2023.01); G06N 3/049 (2013.01); G06N 3/06 (2013.01); G06N 3/063 (2013.01); G06N 3/065 (2023.01); 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

Optimized memory usage and management is crucial to the overall performance of a neural network (NN) or deep neural network (DNN) computing environment. Using various characteristics of the input data dimension, an apportionment sequence is calculated for the input data to be processed by the NN or DNN that optimizes the efficient use of the local and external memory components. The apportionment sequence can describe how to parcel the input data (and its associated processing parameters—e.g., processing weights) into one or more portions as well as how such portions of input data (and its associated processing parameters) are passed between the local memory, external memory, and processing unit components of the NN or DNN. Additionally, the apportionment sequence can include instructions to store generated output data in the local and/or external memory components so as to optimize the efficient use of the local and/or external memory components.


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