Nvidia corporation (20240346337). BOUNDING AREA PLANNING USING A CONGESTION PREDICTION MODEL simplified abstract

BOUNDING AREA PLANNING USING A CONGESTION PREDICTION MODEL

Organization Name

nvidia corporation

Inventor(s)

Tian Yang of Los Altos CA (US)

Shijia Hu of Santa Clara CA (US)

BOUNDING AREA PLANNING USING A CONGESTION PREDICTION MODEL - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240346337 titled 'BOUNDING AREA PLANNING USING A CONGESTION PREDICTION MODEL

Simplified Explanation: The patent application describes a method for planning the layout of integrated circuits by predicting congestion levels in specific areas based on placement data and using a machine learning model.

• Key Features and Innovation:

   - Identification of placement data associated with cells in an IC design.
   - Generation of a graph based on the placement data.
   - Training a machine learning model to predict congestion levels in bounding areas of the IC design.
   - Designation of cells for installation in specific regions based on congestion predictions.

• Potential Applications:

   - Semiconductor industry for optimizing IC layout.
   - Electronic design automation tools for improved circuit design.
   - Telecommunications for network planning based on congestion predictions.

• Problems Solved:

   - Efficient planning of IC layouts to minimize congestion.
   - Enhanced performance and reliability of integrated circuits.
   - Streamlined design process for complex electronic systems.

• Benefits:

   - Improved circuit performance and reliability.
   - Time and cost savings in IC design.
   - Enhanced predictability in congestion management.

• Commercial Applications:

   - "Congestion Prediction Model for IC Layout Optimization in Semiconductor Industry"

• Prior Art:

   - Researchers in the field of electronic design automation and semiconductor manufacturing may have explored similar methods for congestion prediction in IC layouts.

• Frequently Updated Research:

   - Ongoing research in machine learning applications for electronic design automation.
   - Advancements in predictive modeling for congestion management in integrated circuits.

Questions about Congestion Prediction Model for IC Layout Optimization:

1. How does the machine learning model predict congestion levels in specific areas of an IC design? 2. What are the potential implications of using this technology in the semiconductor industry?

2. How does the machine learning model predict congestion levels in specific areas of an IC design?

   - The machine learning model is trained using a graph generated from placement data of cells in an IC design. This graph serves as input for the model to predict congestion levels in different bounding areas of the design.

3. What are the potential implications of using this technology in the semiconductor industry?

   - The technology can lead to more efficient IC layouts, reducing congestion and improving overall performance of integrated circuits. This can result in cost savings and enhanced competitiveness for semiconductor companies.

Original Abstract Submitted

apparatuses, systems, and techniques for bounding area planning using a congestion prediction model. placement data associated with cells of an ic design is identified. a graph based on the identified placement data is generated. the graph is provided as input to a machine learning model. the machine learning model is trained to predict, based on a given graph associated with cells according to a respective ic design, a congestion level for cells at one or more bounding areas of a respective ic design. outputs of the machine learning model are obtained. the outputs include congestion data indicating a congestion level for a first bounding area of the ic design. cells are designated for installation at a region, of the ic design, corresponding to the first bounding area.