View source for 20240054319. Data Flow Method and Apparatus for Neural Network Computation simplified abstract (Zhejiang Lab)

=Data Flow Method and Apparatus for Neural Network Computation=


==Organization Name==

[[:Category:Zhejiang Lab|Zhejiang Lab]]


[[Category:Zhejiang Lab]]

==Inventor(s)==

[[:Category:Hongsheng Wang of Hangzhou (CN)|Hongsheng Wang of Hangzhou (CN)]][[Category:Hongsheng Wang of Hangzhou (CN)]]

[[:Category:Guang Chen of Hangzhou (CN)|Guang Chen of Hangzhou (CN)]][[Category:Guang Chen of Hangzhou (CN)]]

==Data Flow Method and Apparatus for Neural Network Computation - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240054319 titled 'Data Flow Method and Apparatus for Neural Network Computation

==Simplified Explanation==

The patent application describes a data flow method and apparatus for neural network computation in a deep learning training system.

* Initializing the lifecycle of a variable in a computational graph.
* Defining a propagation rule for a variable in use to flow through a node in the computational graph.
* Producing a definition of a variable at a precursor node if the variable is used at a certain node in the computational graph.

### Potential Applications
* Deep learning training systems
* Neural network computation
* Data flow modeling

### Problems Solved
* Efficient management of variable lifecycles in computational graphs
* Improved propagation of variables through nodes in neural networks

### Benefits
* Enhanced performance in deep learning training systems
* More effective neural network computation
* Streamlined data flow modeling for complex systems


==Original Abstract Submitted==

disclosed are a data flow method and apparatus for neural network computation. the method includes: step 1, initializing the lifecycle of a variable in a computational graph, i.e., initializing a time period from the start of a definition of the variable to the end of use as the lifecycle of the variable in the computational graph; and step 2, defining a propagation rule for a variable in use to flow through a node, i.e., defining that in the case that a variable at a certain node in the computational graph is used, a definition of the variable is produced at a precursor node of the node, such that an input set of valid variables flowing through the node contains the variable. the application discloses a data flow modeling method and apparatus for neural network computation in a deep learning training system.



[[Category:G06N3/04]]
[[Category:G06K9/62]]