Samsung electronics co., ltd. (20240346317). NEURAL NETWORK METHOD AND APPARATUS simplified abstract
Contents
NEURAL NETWORK METHOD AND APPARATUS
Organization Name
Inventor(s)
NEURAL NETWORK METHOD AND APPARATUS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240346317 titled 'NEURAL NETWORK METHOD AND APPARATUS
Simplified Explanation: This patent application describes a method and apparatus for pruning a neural network by setting weight threshold values based on the weight distribution of layers, predicting changes in inference accuracy, determining which layer to prune, and then actually pruning that layer.
Key Features and Innovation:
- Method sets weight threshold values based on weight distribution of layers in a neural network.
- Predicts changes in inference accuracy by pruning each layer based on weight threshold values.
- Determines which layer to prune based on weight threshold values.
- Prunes the selected layer from the neural network.
Potential Applications: This technology can be applied in various fields such as:
- Machine learning
- Artificial intelligence
- Data analysis
- Pattern recognition
Problems Solved: This technology addresses the following issues:
- Efficient pruning of neural networks
- Improving inference accuracy
- Optimizing neural network performance
Benefits: The benefits of this technology include:
- Enhanced efficiency in neural network pruning
- Improved accuracy in inference
- Better overall performance of neural networks
Commercial Applications: Title: Neural Network Pruning Technology for Enhanced Performance This technology can be utilized in:
- Developing advanced AI systems
- Enhancing machine learning algorithms
- Improving data processing capabilities
- Optimizing neural network models for various applications
Prior Art: Readers can explore prior research on neural network pruning techniques, weight threshold optimization, and inference accuracy prediction in the field of machine learning and artificial intelligence.
Frequently Updated Research: Stay updated on the latest advancements in neural network pruning techniques, weight threshold optimization strategies, and inference accuracy prediction models to enhance the performance of AI systems.
Questions about Neural Network Pruning: 1. What are the key factors to consider when setting weight threshold values for neural network pruning? 2. How does predicting changes in inference accuracy help in optimizing neural network performance?
Original Abstract Submitted
a method and apparatus for the pruning of a neural network is provided. the method sets a weight threshold value based on a weight distribution of layers included in a neural network, predicts a change of inference accuracy of a neural network by pruning of each layer based on the weight threshold value, determines a current subject layer to be pruned with a weight threshold value among the layers included in the neural network, and prunes a determined current subject layer.
