Boe technology group co., ltd. (20240161474). Neural Network Inference Acceleration Method, Target Detection Method, Device, and Storage Medium simplified abstract
Contents
- 1 Neural Network Inference Acceleration Method, Target Detection Method, Device, and Storage Medium
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Neural Network Inference Acceleration Method, Target Detection Method, Device, and Storage Medium - 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
Neural Network Inference Acceleration Method, Target Detection Method, Device, and Storage Medium
Organization Name
boe technology group co., ltd.
Inventor(s)
Neural Network Inference Acceleration Method, Target Detection Method, Device, and Storage Medium - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240161474 titled 'Neural Network Inference Acceleration Method, Target Detection Method, Device, and Storage Medium
Simplified Explanation
The patent application describes a method for accelerating neural network inference by optimizing the neural network model through processes such as model compression, graph optimization, and deployment optimization. This results in an accelerated neural network model that can be used for inference evaluation.
- Acquiring a neural network model and an accelerated data set
- Automatically performing accelerating processes on the neural network model using the accelerated data set
- Accelerating processes include model compression, graph optimization, and deployment optimization
- Model compression techniques include model quantification, model pruning, and model distillation
- Graph optimization involves optimizing the directed graph of the neural network model
- Deployment optimization focuses on optimizing the deployment platform of the neural network model
- Performing inference evaluation on the accelerated neural network model
Potential Applications
The technology can be applied in various fields such as:
- Autonomous vehicles
- Medical image analysis
- Natural language processing
- Robotics
Problems Solved
The technology helps in addressing the following issues:
- Slow inference speed of neural networks
- High computational resources required for neural network inference
- Large model sizes impacting deployment and execution efficiency
Benefits
The benefits of this technology include:
- Improved inference speed
- Reduced computational resources
- Enhanced deployment efficiency
- Increased performance of neural network models
Potential Commercial Applications
The technology can be commercially applied in industries such as:
- Healthcare
- Automotive
- Finance
- Retail
Possible Prior Art
One possible prior art in this field is the use of hardware accelerators for neural network inference, such as GPUs and TPUs. These accelerators aim to speed up the inference process by offloading computations from the CPU to specialized hardware.
Unanswered Questions
How does this method compare to existing acceleration techniques in terms of speed and efficiency?
The article does not provide a direct comparison with other acceleration techniques, leaving a gap in understanding the relative performance of this method.
What are the potential limitations or drawbacks of implementing this acceleration method?
The article does not discuss any potential limitations or drawbacks of implementing this acceleration method, which could be important factors to consider in practical applications.
Original Abstract Submitted
a neural network inference acceleration method includes: acquiring a neural network model to be accelerated and an accelerated data set; automatically performing accelerating process on the neural network model to be accelerated by using the accelerated data set to obtain the accelerated neural network model, wherein the accelerating process includes at least one of the following: model compression, graph optimization and deployment optimization, wherein the model compression includes at least one of the following: model quantification, model pruning and model distillation, wherein the graph optimization is the optimization for the directed graph of the neural network model to be accelerated, and the deployment optimization is the optimization for the deployment platform of the neural network model to be accelerated; and performing inference evaluation on the accelerated neural network model.
