Micron technology, inc. (20240202494). INTERMEDIATE MODULE NEURAL ARCHITECTURE SEARCH simplified abstract
Contents
- 1 INTERMEDIATE MODULE NEURAL ARCHITECTURE SEARCH
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 INTERMEDIATE MODULE NEURAL ARCHITECTURE SEARCH - 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 Architecture Search
- 1.13 Original Abstract Submitted
INTERMEDIATE MODULE NEURAL ARCHITECTURE SEARCH
Organization Name
Inventor(s)
Andre Xian Ming Chang of Bellevue WA (US)
Abhishek Chaurasia of Redmond WA (US)
INTERMEDIATE MODULE NEURAL ARCHITECTURE SEARCH - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240202494 titled 'INTERMEDIATE MODULE NEURAL ARCHITECTURE SEARCH
Simplified Explanation
The patent application describes a system that searches for candidate modules in a dynamic search space to optimize a neural network model. The system evaluates these modules based on accuracy and runtime execution to propose an optimal model.
Key Features and Innovation
- System searches for candidate modules in a dynamic search space for neural network models.
- Analyzes existing models to determine insertion points for candidate modules.
- Applies a zero-shot metric to rank candidate modules based on their potential to substitute existing modules.
- Trains candidate modules over multiple epochs on a dataset to determine accuracy ranks.
- Executes candidate models on a deep learning accelerator to assess runtime execution ranks.
- Proposes an optimal model based on accuracy and runtime execution ranks.
Potential Applications
The technology can be applied in various fields such as computer vision, natural language processing, and speech recognition to optimize neural network models for better performance.
Problems Solved
The system addresses the challenge of efficiently searching for and integrating candidate modules into neural network models to improve accuracy and runtime execution.
Benefits
- Enhances the performance of neural network models by optimizing the selection of candidate modules.
- Streamlines the process of model optimization by automating the search for optimal configurations.
- Improves the efficiency of deep learning accelerators by selecting models with better runtime execution.
Commercial Applications
Optimizing neural network models using this system can benefit industries such as healthcare, finance, and autonomous vehicles by improving the accuracy and efficiency of AI systems.
Prior Art
Readers interested in prior art related to this technology can explore research papers, patents, and academic publications on neural architecture search and model optimization in deep learning.
Frequently Updated Research
Stay updated on the latest advancements in neural architecture search, model optimization, and deep learning accelerators to further enhance the performance of AI systems.
Questions about Neural Architecture Search
How does the system determine the insertion points for candidate modules in existing models?
The system analyzes the structure of the existing model to identify suitable locations where candidate modules can be inserted for evaluation.
What is the significance of applying a zero-shot metric to rank candidate modules?
The zero-shot metric helps prioritize candidate modules based on their potential to substitute existing modules without the need for extensive training or fine-tuning.
Original Abstract Submitted
a system providing intermediate module neural architecture search is disclosed. the system searches a dynamic search space for candidate modules for a model of a neural network. the system analyzes an existing model and determines an insertion point at which the candidate modules may be inserted. a zero-shot metric is applied to the candidate modules to generate a ranking of candidate modules that may substitute an existing module at the insertion point. the system trains the candidate modules over a plurality of epochs on a distribution of data of a dataset. based on the training, the system determines an accuracy rank for each of the candidate modules. the system executes candidate models including the candidate modules on a deep learning accelerator to determine a runtime execution rank for the candidate models. based on the accuracy and runtime execution ranks, the system determines an optimal proposed model from the candidate models.
