Intel corporation (20240104378). DYNAMIC QUANTIZATION OF NEURAL NETWORKS simplified abstract
Contents
- 1 DYNAMIC QUANTIZATION OF NEURAL NETWORKS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 DYNAMIC QUANTIZATION OF 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
DYNAMIC QUANTIZATION OF NEURAL NETWORKS
Organization Name
Inventor(s)
Michael E. Deisher of Hillsboro OR (US)
DYNAMIC QUANTIZATION OF NEURAL NETWORKS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240104378 titled 'DYNAMIC QUANTIZATION OF NEURAL NETWORKS
Simplified Explanation
The apparatus described in the patent application is designed for applying dynamic quantization of a neural network. Here is a simplified explanation of the abstract:
- The apparatus includes a scaling unit and a quantizing unit.
- The scaling unit calculates initial desired scale factors for inputs, weights, and bias, and applies the input scale factor to a summation node.
- It determines scale factors for a multiplication node based on desired scale factors of the inputs and selects scale factors for an activation function and an output node.
- The quantizing unit dynamically requantizes the neural network by traversing a graph of the network.
Potential Applications
The technology described in this patent application could be applied in various fields such as:
- Artificial intelligence
- Machine learning
- Neural network optimization
Problems Solved
This technology addresses several challenges in neural network optimization, including:
- Improving efficiency of neural network operations
- Enhancing accuracy of neural network predictions
- Reducing computational complexity in neural network training
Benefits
The benefits of this technology include:
- Faster neural network computations
- Enhanced performance of neural network models
- Improved scalability of neural network systems
Potential Commercial Applications
Potential commercial applications of this technology could include:
- Developing advanced AI systems
- Optimizing machine learning algorithms
- Enhancing neural network-based products and services
Possible Prior Art
One possible prior art in this field is the use of static quantization techniques in neural networks to reduce computational costs and memory requirements.
What are the potential limitations of this technology in real-world applications?
One potential limitation of this technology in real-world applications could be the complexity of implementing dynamic quantization in existing neural network architectures. This may require significant modifications to the network structure and training processes.
How does this technology compare to existing methods of neural network quantization?
This technology offers the advantage of dynamically adjusting the quantization levels of a neural network based on the desired scale factors of inputs, weights, and bias. This dynamic approach may lead to improved performance and efficiency compared to static quantization methods.
Original Abstract Submitted
an apparatus for applying dynamic quantization of a neural network is described herein. the apparatus includes a scaling unit and a quantizing unit. the scaling unit is to calculate an initial desired scale factors of a plurality of inputs, weights and a bias and apply the input scale factor to a summation node. also, the scaling unit is to determine a scale factor for a multiplication node based on the desired scale factors of the inputs and select a scale factor for an activation function and an output node. the quantizing unit is to dynamically requantize the neural network by traversing a graph of the neural network.
