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. 29, 2022

Filed:

Mar. 29, 2019
Applicant:

Xilinx, Inc., San Jose, CA (US);

Inventors:

Lingzhi Sui, Beijing, CN;

Yushun Wang, Beijing, CN;

Xin Liu, Beijing, CN;

Assignee:

Other;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/08 (2006.01); G06N 3/04 (2006.01);
U.S. Cl.
CPC ...
G06N 3/082 (2013.01); G06N 3/04 (2013.01);
Abstract

The present invention discloses a method to optimize a neural network computational graph. The computational graph is used for performing neural network calculation by a computational platform. The computational platform reads data needed by the calculation from off-chip memory. The method comprises: layers which can be fused are selected at least based on an optimization rule to reduce frequency of data exchange between the computational platform and the off-chip memory, carrying out fusion for at least two adjacent layers in the computational graph according to the selected layer objects. Here, the at least two adjacent layers are at least one of the following: horizontally adjacent layers having the same input of feature maps; and longitudinally adjacent layers in which the calculation results of a feature map of a previous layer are at least part of input for a next layer. The method to optimize a computational graph of the present invention can be automatically carried out based on rules or through isomorphic subgraph matching. Thus, an optimal reconstruction mode for executing the computational graph is found out, execution efficiency of the neural network computational platform is improved.


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