Nvidia corporation (20240119291). DYNAMIC NEURAL NETWORK MODEL SPARSIFICATION simplified abstract

DYNAMIC NEURAL NETWORK MODEL SPARSIFICATION

Organization Name

nvidia corporation

Inventor(s)

Jose M. Alvarez Lopez of Mountain View CA (US)

Pavlo Molchanov of Mountain View CA (US)

Hongxu Yin of San Jose CA (US)

Maying Shen of Santa Clara CA (US)

Lei Mao of San Jose CA (US)

Xinglong Sun of Menlo Park CA (US)

DYNAMIC NEURAL NETWORK MODEL SPARSIFICATION - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240119291 titled 'DYNAMIC NEURAL NETWORK MODEL SPARSIFICATION

Simplified Explanation

The abstract describes a patent application for a dynamic neural network model sparsification process that allows for recovery of previously pruned parts to improve the quality of the sparse neural network model.

• The patent application focuses on a compression technique known as model sparsification to reduce the size, computation, and latency of a neural network model.
• The innovation provides a process that addresses the negative consequences of pruning a fully pretrained neural network model and training a sparse model without any recovery option.
• The dynamic neural network model sparsification process enables the improvement of the quality of the sparse neural network model by allowing for the recovery of previously pruned parts.

Potential Applications

The technology can be applied in various fields such as:

• Image recognition
• Natural language processing
• Autonomous vehicles

Problems Solved

The technology addresses the following issues:

• Negative consequences of pruning a fully pretrained neural network model
• Lack of recovery options when training a sparse model

Benefits

The benefits of this technology include:

• Improved efficiency of neural network models
• Reduction in size, computation, and latency
• Enhanced performance of sparse neural network models

Potential Commercial Applications

The technology can be utilized in industries such as:

• Healthcare for medical image analysis
• Finance for fraud detection
• Manufacturing for quality control

Possible Prior Art

One possible prior art is the use of traditional model compression techniques such as weight pruning and quantization to reduce the size of neural network models.

Unanswered Questions

How does the dynamic neural network model sparsification process compare to other model compression techniques in terms of performance and efficiency?

The article does not provide a direct comparison between the dynamic neural network model sparsification process and other model compression techniques.

Are there any limitations or drawbacks to the dynamic neural network model sparsification process that have not been addressed in the article?

The article does not mention any potential limitations or drawbacks of the dynamic neural network model sparsification process.

Original Abstract Submitted

machine learning is a process that learns a neural network model from a given dataset, where the model can then be used to make a prediction about new data. in order to reduce the size, computation, and latency of a neural network model, a compression technique can be employed which includes model sparsification. to avoid the negative consequences of pruning a fully pretrained neural network model and on the other hand of training a sparse model in the first place without any recovery option, the present disclosure provides a dynamic neural network model sparsification process which allows for recovery of previously pruned parts to improve the quality of the sparse neural network model.