Nvidia corporation (20240160932). TECHNIQUES FOR PRUNING NEURAL NETWORKS simplified abstract
Contents
- 1 TECHNIQUES FOR PRUNING NEURAL NETWORKS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 TECHNIQUES FOR PRUNING NEURAL NETWORKS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
TECHNIQUES FOR PRUNING NEURAL NETWORKS
Organization Name
Inventor(s)
TECHNIQUES FOR PRUNING NEURAL NETWORKS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240160932 titled 'TECHNIQUES FOR PRUNING NEURAL NETWORKS
Simplified Explanation
The abstract of the patent application describes apparatuses, systems, and techniques for pruning neural networks by deactivating one or more portions of the network based on less than all previously evaluated portions.
- Neural network pruning involves deactivating certain portions of the network.
- Deactivation is based on evaluation of only some, not all, previously assessed portions.
Potential Applications
This technology could be applied in:
- Improving the efficiency of neural networks.
- Reducing computational resources required for neural network operations.
Problems Solved
This technology addresses:
- Overly complex neural networks.
- Resource-intensive neural network operations.
Benefits
The benefits of this technology include:
- Increased efficiency in neural network operations.
- Reduction in computational resources needed for neural networks.
Potential Commercial Applications
A potential commercial application of this technology could be in:
- Developing more streamlined and efficient neural network models for various industries.
Possible Prior Art
One possible prior art in neural network pruning is the work done by Han et al. in their paper "Learning both Weights and Connections for Efficient Neural Networks" published in 2015.
What are the specific techniques used for neural network pruning in this patent application?
The specific techniques used for neural network pruning in this patent application involve deactivating one or more portions of the network based on less than all previously evaluated portions.
How does this technology compare to existing methods of neural network pruning?
This technology improves upon existing methods of neural network pruning by selectively deactivating portions of the network based on partial evaluations, leading to increased efficiency and reduced computational resources.
Original Abstract Submitted
apparatuses, systems, and techniques to prune neural networks. in at least one embodiment, one or more portions of a neural network are deactivated based, at least in part, on less than all previously evaluated portions of the neural network.
