Micron technology, inc. (20240201957). NEURAL NETWORK MODEL DEFINITION CODE GENERATION AND OPTIMIZATION simplified abstract
Contents
NEURAL NETWORK MODEL DEFINITION CODE GENERATION AND OPTIMIZATION
Organization Name
Inventor(s)
Abhishek Chaurasia of Redmond WA (US)
Andre Xian Ming Chang of Bellevue WA (US)
NEURAL NETWORK MODEL DEFINITION CODE GENERATION AND OPTIMIZATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240201957 titled 'NEURAL NETWORK MODEL DEFINITION CODE GENERATION AND OPTIMIZATION
Simplified Explanation:
The system described in the patent application generates neural network model definition code and optimizes it based on various inputs. These inputs can include freehand drawings of a model, modules from repositories, and other forms of content. The system uses a neural network to analyze these inputs and create a graph representing the artificial intelligence model. It then selects properties of the model, locates modules, generates code, and creates the model definition. Once completed, the artificial intelligence model can perform specific tasks.
- The system generates neural network model definition code and optimizes it based on various inputs.
- Inputs can include freehand drawings, modules from repositories, and other forms of content.
- A neural network is used to analyze the inputs and create a graph for the artificial intelligence model.
- Properties of the model are selected, modules are located, code is generated, and the model definition is created.
- The artificial intelligence model can then perform specific tasks.
Potential Applications: - This technology can be applied in various industries such as healthcare, finance, and autonomous vehicles. - It can be used for image recognition, natural language processing, and predictive analytics.
Problems Solved: - Simplifies the process of generating neural network model definitions. - Optimizes the code generation for artificial intelligence models. - Enhances the efficiency and accuracy of creating AI models.
Benefits: - Reduces the time and effort required to develop artificial intelligence models. - Improves the performance and functionality of AI applications. - Enables faster deployment of AI solutions in different domains.
Commercial Applications: Title: "Neural Network Model Definition Code Generation System for AI Applications" This technology can be commercially used in software development companies, research institutions, and tech startups to streamline the creation of AI models. It can have significant market implications by accelerating the development of AI-powered products and services.
Prior Art: Readers can explore prior research on neural network model generation, code optimization for AI, and graph-based AI model development to understand the existing knowledge in this field.
Frequently Updated Research: Stay updated on advancements in neural network algorithms, AI model optimization techniques, and graph-based AI model generation methods to enhance the efficiency and effectiveness of this technology.
Questions about Neural Network Model Definition Code Generation System for AI Applications: 1. How does the system determine the properties of the artificial intelligence model? 2. What are the key advantages of using a neural network for analyzing inputs in this system?
Original Abstract Submitted
a system providing neural network model definition code generation and optimization is disclosed. the system receives inputs to facilitate the generation of an artificial intelligence model, such as freehand drawings of a model, modules available in repositories, various forms of content, and other inputs. the system utilizes a neural network to analyze the inputs and generates blocks and connections to generate a graph for the artificial intelligence model. properties of the model are selected, and the system locates modules, generates code for modules, or both, based on the blocks and connections from the graph and the properties. the system generates the model definition for the artificial intelligence model using the located modules and the generated code. once the model definition is completed, the artificial intelligence model may be utilized to perform a task for which the artificial intelligence model has been created to perform.
