18437370. METHOD AND APPARATUS FOR NEURAL NETWORK QUANTIZATION simplified abstract (SAMSUNG ELECTRONICS CO., LTD.)
Contents
- 1 METHOD AND APPARATUS FOR NEURAL NETWORK QUANTIZATION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD AND APPARATUS FOR NEURAL NETWORK QUANTIZATION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Neural Network Quantization
- 1.13 Original Abstract Submitted
METHOD AND APPARATUS FOR NEURAL NETWORK QUANTIZATION
Organization Name
Inventor(s)
Seungwon Lee of Hwaseong-si (KR)
Junhaeng Lee of Hwaseong-si (KR)
METHOD AND APPARATUS FOR NEURAL NETWORK QUANTIZATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 18437370 titled 'METHOD AND APPARATUS FOR NEURAL NETWORK QUANTIZATION
Simplified Explanation
The patent application describes a method and apparatus for quantizing a neural network by analyzing weight differences and determining layers to be quantized with lower-bit precision.
- Learning of a neural network is performed.
- Weight differences between initial and updated weights are obtained for each cycle.
- Statistics of weight differences for each layer are analyzed.
- One or more layers are determined to be quantized with lower-bit precision based on the analyzed statistics.
- A second neural network is generated by quantizing the determined layers with lower-bit precision.
Key Features and Innovation
- Method for generating a quantized neural network based on weight analysis.
- Determination of layers to be quantized with lower-bit precision.
- Optimization of neural network for efficiency and performance.
Potential Applications
- Machine learning algorithms.
- Edge computing devices.
- IoT devices.
- Robotics.
Problems Solved
- Reduction of memory and computational requirements.
- Improved efficiency of neural networks.
- Enhanced performance of machine learning models.
Benefits
- Faster inference times.
- Reduced energy consumption.
- Improved accuracy of neural networks.
Commercial Applications
Quantized Neural Network Optimization for Edge Devices This technology can be utilized in edge computing devices to improve the efficiency and performance of machine learning models, catering to the growing demand for AI-powered applications in IoT devices and robotics.
Prior Art
Information on prior art related to this technology is not available at the moment.
Frequently Updated Research
There is ongoing research in the field of neural network quantization to further enhance the efficiency and performance of machine learning models.
Questions about Neural Network Quantization
Question 1
How does quantizing neural networks with lower-bit precision impact model accuracy?
Quantizing neural networks with lower-bit precision can lead to a slight decrease in model accuracy but can significantly reduce memory and computational requirements, making them more suitable for deployment on resource-constrained devices.
Question 2
What are the challenges associated with determining the layers to be quantized in a neural network?
One of the challenges is finding the right balance between reducing precision to improve efficiency without compromising model performance. Additionally, the selection of layers to be quantized requires careful analysis of weight differences and their impact on overall network performance.
Original Abstract Submitted
According to a method and apparatus for neural network quantization, a quantized neural network is generated by performing learning of a neural network, obtaining weight differences between an initial weight and an updated weight determined by the learning of each cycle for each of layers in the first neural network, analyzing a statistic of the weight differences for each of the layers, determining one or more layers, from among the layers, to be quantized with a lower-bit precision based on the analyzed statistic, and generating a second neural network by quantizing the determined one or more layers with the lower-bit precision.
