18098061. TECHNIQUES FOR PRUNING NEURAL NETWORKS simplified abstract (NVIDIA Corporation)
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 Unanswered Questions
- 1.11 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 18098061 titled 'TECHNIQUES FOR PRUNING NEURAL NETWORKS
Simplified Explanation
The abstract describes a patent application related to apparatuses, systems, and techniques for pruning neural networks by deactivating certain portions based on previous evaluations.
- Neural network pruning involves deactivating specific portions based on past evaluations.
- The deactivation is done on less than all previously evaluated portions of the neural network.
Potential Applications
This technology could be applied in various fields such as:
- Machine learning
- Artificial intelligence
- Data analysis
Problems Solved
This technology helps in:
- Improving the efficiency of neural networks
- Reducing computational resources required for neural network operations
Benefits
The benefits of this technology include:
- Faster neural network operations
- Reduced energy consumption
- Improved performance of neural networks
Potential Commercial Applications
Potential commercial applications of this technology could include:
- Developing faster and more efficient AI systems
- Enhancing data processing capabilities in various industries
Possible Prior Art
One example of prior art in neural network pruning is the work done by Han et al. in 2015 on deep compression techniques for neural networks.
Unanswered Questions
How does this technology compare to other neural network pruning methods in terms of efficiency and accuracy?
This article does not provide a direct comparison with other pruning methods, leaving the reader to wonder about the relative performance of this technology.
Are there any limitations to the size or complexity of neural networks that can be pruned using this technology?
The article does not address any potential limitations in terms of the size or complexity of neural networks that can benefit from this pruning technique.
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.
