Taiwan semiconductor manufacturing company, ltd. (20240338506). SYSTEM AND METHOD FOR ESL MODELING OF MACHINE LEARNING simplified abstract
Contents
SYSTEM AND METHOD FOR ESL MODELING OF MACHINE LEARNING
Organization Name
taiwan semiconductor manufacturing company, ltd.
Inventor(s)
Sandeep Kumar Goel of Hsinchu (TW)
Tze-Chiang Huang of Hsichu (TW)
SYSTEM AND METHOD FOR ESL MODELING OF MACHINE LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240338506 titled 'SYSTEM AND METHOD FOR ESL MODELING OF MACHINE LEARNING
The patent application describes a computer-readable storage medium with instructions for designing a semiconductor device using electronic system level (esl) modeling for machine learning applications.
- Retrieve source code for executing machine learning algorithm operations.
- Classify operations into slow and fast groups based on completion time.
- Define a neural network for executing slow group operations.
- Create a trained neural network configuration for executing slow group operations.
- Generate an esl platform for evaluating semiconductor device designs based on the trained neural network configuration.
- Potential Applications:**
- Semiconductor device design optimization. - Machine learning algorithm acceleration. - Electronic system level modeling advancements.
- Problems Solved:**
- Efficient classification and execution of machine learning algorithm operations. - Improved semiconductor device design evaluation process. - Enhanced performance in machine learning applications.
- Benefits:**
- Faster execution of slow group operations. - More accurate evaluation of semiconductor device designs. - Increased efficiency in machine learning applications.
- Commercial Applications:**
Title: "Enhanced Semiconductor Device Design with Machine Learning Acceleration" This technology can be utilized in industries such as semiconductor manufacturing, artificial intelligence development, and electronic system design. It can lead to faster product development cycles, improved performance in machine learning applications, and enhanced competitiveness in the market.
- Questions about the Technology:**
1. How does this technology improve the efficiency of machine learning algorithm execution? 2. What are the potential implications of using esl modeling for semiconductor device design optimization?
Original Abstract Submitted
a non-transitory computer-readable storage medium is encoded with a set of instructions for designing a semiconductor device using electronic system level (esl) modeling for machine learning applications that, when executed by at least one processor, cause the at least one processor to: retrieve a source code operable to execute a plurality of operations of a machine learning algorithm; classify a first group of the plurality of operations as slow group operations and classify a second group of the plurality of operations as fast group operations, based on a time required to complete each operation; define a neural network operable to execute the slow group operations; define a trained neural network configuration including a plurality of interconnected neurons operable to execute the slow group operations; and generate an esl platform for evaluating a design of a semiconductor device based on the trained neural network configuration.
