View source for 17950449. ARTIFICIAL NEURAL NETWORK MODULE FOR PERFORMING ARTIFICIAL NEURAL NETWORK OPERATION ON PLURALITY OF SUBGRAPHS AND OPERATING METHOD THEREOF simplified abstract (Samsung Electronics Co., Ltd.)

=ARTIFICIAL NEURAL NETWORK MODULE FOR PERFORMING ARTIFICIAL NEURAL NETWORK OPERATION ON PLURALITY OF SUBGRAPHS AND OPERATING METHOD THEREOF=


==Organization Name==

[[:Category:Samsung Electronics Co., Ltd.|Samsung Electronics Co., Ltd.]]


[[Category:Samsung Electronics Co., Ltd.]]

==Inventor(s)==

[[:Category:Seungsoo Yang of Hwaseong-si (KR)|Seungsoo Yang of Hwaseong-si (KR)]][[Category:Seungsoo Yang of Hwaseong-si (KR)]]

==ARTIFICIAL NEURAL NETWORK MODULE FOR PERFORMING ARTIFICIAL NEURAL NETWORK OPERATION ON PLURALITY OF SUBGRAPHS AND OPERATING METHOD THEREOF - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17950449 titled 'ARTIFICIAL NEURAL NETWORK MODULE FOR PERFORMING ARTIFICIAL NEURAL NETWORK OPERATION ON PLURALITY OF SUBGRAPHS AND OPERATING METHOD THEREOF

==Simplified Explanation==

The abstract describes a method for operating an artificial neural network on multiple subgraphs. Here is a simplified explanation:

* The method involves generating a trigger for determining the resources needed for a specific subgraph within a neural network model.
* A control signal is then generated to allocate hardware resources to the target subgraph based on the trigger.
* The hardware allocation and resource settings are adjusted according to the control signal.
* Finally, an operation is performed on the target subgraph using the modified hardware and resource settings.

== Potential Applications ==
This technology has potential applications in various fields, including:

* Machine learning: The method can be used to optimize the performance of artificial neural networks, improving their accuracy and efficiency.
* Computer vision: By efficiently allocating resources to different subgraphs, this method can enhance the processing of visual data, enabling better object recognition and image analysis.
* Natural language processing: The technique can be applied to neural networks used for language processing tasks, such as speech recognition or sentiment analysis, improving their overall performance.

== Problems Solved ==
The method addresses several problems in neural network operations, including:

* Resource allocation: By dynamically allocating hardware resources to different subgraphs, the method optimizes the utilization of available resources, leading to improved performance and efficiency.
* Scalability: The approach allows for the operation of large neural network models with multiple subgraphs, ensuring efficient processing and avoiding bottlenecks.
* Flexibility: The method enables the adaptation of hardware and resource settings based on the specific requirements of each subgraph, allowing for better customization and performance optimization.

== Benefits ==
The use of this technology offers several benefits, including:

* Improved performance: By allocating resources based on the specific needs of each subgraph, the method enhances the overall performance of the neural network, leading to better accuracy and faster processing.
* Efficient resource utilization: The dynamic allocation of hardware resources ensures that they are optimally utilized, reducing wastage and improving the overall efficiency of the system.
* Scalability and adaptability: The method allows for the operation of large neural network models and can adapt to changing requirements, making it suitable for a wide range of applications.


==Original Abstract Submitted==

A method for an artificial neural network operation on a plurality of subgraphs may include generating a resource determination trigger corresponding to a target subgraph among the plurality of subgraphs included in a target neural network model; generating a control signal for hardware allocated to the target subgraph and driving resource settings in response to the resource determination trigger; changing, based on the control signal, at least one of hardware allocated to the target subgraph and driving resource settings; and performing an operation on the target subgraph based on the changed hardware and driving resource settings.



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