Samsung electronics co., ltd. (20240185029). METHOD AND APPARATUS FOR NEURAL NETWORK QUANTIZATION simplified abstract
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 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 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 20240185029 titled 'METHOD AND APPARATUS FOR NEURAL NETWORK QUANTIZATION
Simplified Explanation
The abstract describes a method and apparatus for neural network quantization, where a quantized neural network is generated by analyzing weight differences, determining layers to be quantized with lower-bit precision, and generating a second neural network with the quantized layers.
- Learning of a neural network is performed to obtain weight differences between initial and updated weights.
- Weight differences for each layer are analyzed statistically.
- One or more layers are determined to be quantized with lower-bit precision based on the analyzed statistic.
- A second neural network is generated by quantizing the determined layers with lower-bit precision.
Potential Applications
The technology can be applied in various fields such as:
- Edge computing
- Internet of Things (IoT) devices
- Mobile applications
Problems Solved
This technology addresses the following issues:
- Reducing memory and computational requirements of neural networks
- Improving efficiency and speed of neural network operations
Benefits
The benefits of this technology include:
- Optimizing neural network performance
- Enhancing energy efficiency in neural network applications
Potential Commercial Applications
The technology can be commercially applied in:
- Autonomous vehicles
- Healthcare diagnostics
- Speech recognition systems
Possible Prior Art
One possible prior art for this technology could be:
- Research papers on neural network quantization techniques from academic institutions.
What are the potential limitations of this technology?
Potential limitations of this technology may include:
- Loss of precision in quantized layers leading to reduced accuracy.
- Complexity in determining the optimal layers for quantization.
How does this technology compare to existing neural network quantization methods?
This technology stands out by:
- Utilizing statistical analysis of weight differences to determine layers for quantization.
- Generating a second neural network with quantized layers based on the analyzed statistics.
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.
