Nvidia corporation (20240119267). GENERATING NEURAL NETWORKS simplified abstract

GENERATING NEURAL NETWORKS

Organization Name

nvidia corporation

Inventor(s)

Slawomir Kierat of Warsaw (PL)

Piotr Karpinski of Warsaw (PL)

Mateusz Sieniawski of Warsaw (PL)

Pawel Morkisz of San Jose CA (US)

Szymon Migacz of Santa Clara CA (US)

Linnan Wang of Pleasanton CA (US)

Chen-Han Yu of Mountain House CA (US)

Satish Salian of Santa Clara CA (US)

Ashwath Aithal of Fremont CA (US)

Alexandru Fit-florea of Los Altos Hills CA (US)

GENERATING NEURAL NETWORKS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240119267 titled 'GENERATING NEURAL NETWORKS

Simplified Explanation

The patent application abstract describes apparatuses, systems, and techniques for selectively using one or more neural network layers based on increasing performance metrics.

• Neural network layers are selectively used based on performance metrics
• Performance metrics are iteratively increased to determine which layers to use

Potential Applications

This technology could be applied in various fields such as:

• Image recognition
• Natural language processing
• Autonomous vehicles

Problems Solved

This technology addresses the following issues:

• Optimizing neural network performance
• Efficient resource allocation in neural networks

Benefits

The benefits of this technology include:

• Improved accuracy in neural network predictions
• Enhanced efficiency in neural network operations

Potential Commercial Applications

With its ability to optimize neural network performance, this technology could be valuable in industries such as:

• Healthcare
• Finance
• E-commerce

Possible Prior Art

One possible prior art for this technology could be the use of performance metrics to optimize neural network layers in machine learning applications.

How does this technology compare to existing methods of neural network optimization?

This article does not provide a direct comparison to existing methods of neural network optimization. It would be beneficial to understand the specific advantages of this approach over traditional methods.

What are the potential limitations or challenges of implementing this technology in real-world applications?

The article does not address any potential limitations or challenges that may arise when implementing this technology in practical settings. It would be important to consider factors such as computational resources, data requirements, and scalability issues.

Original Abstract Submitted

apparatuses, systems, and techniques to selectively use one or more neural network layers. in at least one embodiment, one or more neural network layers are selectively used based on, for example, one or more iteratively increasing neural network performance metrics.