Boe technology group co., ltd. (20240242076). METHOD AND DEVICE FOR TESTING DEEP LEARNING MODEL AND COMPUTER STORAGE MEDIUM simplified abstract
Contents
- 1 METHOD AND DEVICE FOR TESTING DEEP LEARNING MODEL AND COMPUTER STORAGE MEDIUM
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD AND DEVICE FOR TESTING DEEP LEARNING MODEL AND COMPUTER STORAGE MEDIUM - 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 Deep Learning Model Testing
- 1.13 Original Abstract Submitted
METHOD AND DEVICE FOR TESTING DEEP LEARNING MODEL AND COMPUTER STORAGE MEDIUM
Organization Name
boe technology group co., ltd.
Inventor(s)
METHOD AND DEVICE FOR TESTING DEEP LEARNING MODEL AND COMPUTER STORAGE MEDIUM - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240242076 titled 'METHOD AND DEVICE FOR TESTING DEEP LEARNING MODEL AND COMPUTER STORAGE MEDIUM
Simplified Explanation
The patent application describes a method, apparatus, and computer storage medium for testing a deep learning model, automating the process of accelerating and testing the model.
- Acquiring a deep learning model for deployment.
- Accelerating the model based on user-specified instructions to improve inference speed.
- Acquiring test samples after acceleration.
- Testing the deep learning model using the test samples.
Key Features and Innovation
- Automatic process for accelerating and testing deep learning models.
- User-specified acceleration instructions.
- Improved inference speed.
- Efficient testing methodology.
Potential Applications
The technology can be applied in various industries such as healthcare, finance, autonomous vehicles, and more where deep learning models are used for decision-making processes.
Problems Solved
- Manual acceleration and testing processes are time-consuming.
- Lack of efficient methods to improve inference speed.
- Difficulty in testing deep learning models effectively.
Benefits
- Time-saving automation.
- Enhanced performance of deep learning models.
- Streamlined testing procedures.
Commercial Applications
Title: Automated Deep Learning Model Testing for Enhanced Performance The technology can be utilized by tech companies, research institutions, and businesses relying on deep learning models for various applications. It can improve efficiency, accuracy, and speed of model deployment and testing, leading to better outcomes and cost savings.
Prior Art
Readers can explore prior research on deep learning model acceleration, testing methodologies, and automation in the field of artificial intelligence and machine learning.
Frequently Updated Research
Researchers are constantly developing new techniques and tools for accelerating and testing deep learning models. Stay updated on recent advancements in the field to leverage the latest innovations for improved model performance.
Questions about Deep Learning Model Testing
How does the automation process improve the efficiency of testing deep learning models?
The automation process accelerates the model and streamlines the testing procedures, saving time and resources compared to manual methods.
What are the potential implications of improved inference speed in deep learning models for real-world applications?
Improved inference speed can lead to faster decision-making processes in various industries, enhancing productivity and performance.
Original Abstract Submitted
the present disclosure discloses a method, apparatus and computer storage medium for testing a deep learning model, which provides an automatic process for accelerating and testing the deep learning model. the method includes acquiring a deep learning model to be deployed; accelerating, in response to an acceleration instruction specified by a user, the deep learning model according to an acceleration method corresponding to the acceleration instruction so as to improve an inference speed of the deep learning model; acquiring test samples corresponding to the deep learning model after the acceleration is finished; and testing the deep learning model by using the test samples.
