View source for Dell products l.p. (20240202316). METHOD, DEVICE AND COMPUTER PROGRAM PRODUCT FOR GENERATING NEURAL NETWORK MODEL simplified abstract

=METHOD, DEVICE AND COMPUTER PROGRAM PRODUCT FOR GENERATING NEURAL NETWORK MODEL=


==Organization Name==

[[:Category:dell products l.p.|dell products l.p.]]


[[Category:dell products l.p.]]

==Inventor(s)==

[[:Category:Tianxiang Chen of Shanghai (CN)|Tianxiang Chen of Shanghai (CN)]][[Category:Tianxiang Chen of Shanghai (CN)]]

[[:Category:Jinpeng Liu of Shanghai (CN)|Jinpeng Liu of Shanghai (CN)]][[Category:Jinpeng Liu of Shanghai (CN)]]

[[:Category:Anzhou Hou of Shanghai (CN)|Anzhou Hou of Shanghai (CN)]][[Category:Anzhou Hou of Shanghai (CN)]]

[[:Category:Zhen Jia of Shanghai (CN)|Zhen Jia of Shanghai (CN)]][[Category:Zhen Jia of Shanghai (CN)]]

==METHOD, DEVICE AND COMPUTER PROGRAM PRODUCT FOR GENERATING NEURAL NETWORK MODEL - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240202316 titled 'METHOD, DEVICE AND COMPUTER PROGRAM PRODUCT FOR GENERATING NEURAL NETWORK MODEL

The abstract describes a method, device, and computer program product for generating a neural network model. The method involves dividing the neural network model into multiple parts, converting code based on syntax for a trusted execution environment, compiling the code, and arranging it in different execution environments for generating the model.

* Method for generating a neural network model by dividing it into multiple parts and arranging them in trusted and untrusted execution environments.
* Conversion of code based on syntax for a trusted execution environment to ensure secure processing.
* Compilation of the converted code to create the neural network model.
* Utilization of different execution environments for processing different parts of the neural network model.
* Enhanced security and efficiency in generating neural network models.

Potential Applications:
- Secure processing of neural network models in trusted environments.
- Efficient compilation and arrangement of neural network code for improved performance.
- Integration of syntax for trusted execution environments in machine learning applications.

Problems Solved:
- Ensuring secure processing of neural network models.
- Optimizing code compilation for neural networks.
- Enhancing efficiency in generating neural network models.

Benefits:
- Improved security in processing sensitive data.
- Enhanced performance and efficiency in neural network model generation.
- Seamless integration of trusted execution environments in machine learning applications.

Commercial Applications:
Title: Secure Neural Network Model Generation for Machine Learning Applications
This technology can be applied in industries such as cybersecurity, finance, healthcare, and autonomous vehicles where secure and efficient neural network model generation is crucial for data processing and decision-making.

Questions about Secure Neural Network Model Generation:
1. How does the method ensure the security of processing neural network models in trusted environments?
2. What are the potential implications of utilizing different execution environments for generating neural network models?


==Original Abstract Submitted==

illustrative embodiments relate to a method, a device, and a computer program product for generating a neural network model. the method includes dividing the neural network model into multiple parts, wherein the multiple parts include a first part for processing an input to the neural network model and a second part for receiving an output from the first part. the method further includes converting, based on syntax for a trusted execution environment, a first part of code in source code of the neural network model and corresponding to the first part. the method further includes compiling the converted first part of code and a second part of code in the source code and corresponding to the second part; and arranging the compiled first part of code and the compiled second part of code respectively in the trusted execution environment and an untrusted execution environment for generating the neural network model.



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