Nvidia corporation (20240104283). TECHNIQUES FOR GENERATING DESIGNS OF CIRCUITS THAT INCLUDE BUFFERS USING MACHINE LEARNING simplified abstract
Contents
- 1 TECHNIQUES FOR GENERATING DESIGNS OF CIRCUITS THAT INCLUDE BUFFERS USING MACHINE LEARNING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 TECHNIQUES FOR GENERATING DESIGNS OF CIRCUITS THAT INCLUDE BUFFERS USING MACHINE LEARNING - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
TECHNIQUES FOR GENERATING DESIGNS OF CIRCUITS THAT INCLUDE BUFFERS USING MACHINE LEARNING
Organization Name
Inventor(s)
Rongjian Liang of Austin TX (US)
TECHNIQUES FOR GENERATING DESIGNS OF CIRCUITS THAT INCLUDE BUFFERS USING MACHINE LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240104283 titled 'TECHNIQUES FOR GENERATING DESIGNS OF CIRCUITS THAT INCLUDE BUFFERS USING MACHINE LEARNING
Simplified Explanation
The abstract describes techniques for designing a circuit using machine learning models to predict buffer size, location, and delay targets. The circuit design is based on a generated tree structure representing the driver, sinks, and buffers.
- Receiving a specification for a driver and multiple sinks
- Executing a machine learning model to predict buffer characteristics
- Generating a tree structure representing the components of the circuit
- Designing the circuit based on the tree structure
Potential Applications
This technology could be applied in the design of integrated circuits, especially in optimizing buffer placement and sizing for improved performance.
Problems Solved
This technology helps in automating the circuit design process, reducing manual effort, and potentially improving circuit performance by optimizing buffer characteristics.
Benefits
The use of machine learning models can lead to more efficient and effective circuit designs, potentially reducing design time and improving overall circuit performance.
Potential Commercial Applications
This technology could be valuable in the semiconductor industry for designing complex integrated circuits with optimized buffer configurations.
Possible Prior Art
One possible prior art could be traditional circuit design methods that rely on manual placement and sizing of buffers based on heuristics and experience.
Unanswered Questions
How does this technology compare to traditional circuit design methods?
This article does not directly compare the performance or efficiency of this technology with traditional circuit design methods.
What are the limitations of using machine learning models in circuit design?
The article does not discuss any potential limitations or challenges of using machine learning models for circuit design.
Original Abstract Submitted
techniques are disclosed herein for designing a circuit. the techniques include receiving a specification for a driver and a plurality of sinks; executing, based on the driver and the plurality of sinks, a machine learning model that predicts at least one of a size, a location, or a delay target of one or more buffers; generating a tree that includes a plurality of nodes representing the driver, the plurality of sinks, and the one or more buffers between the driver and one or more of the sinks; and generating a design of a circuit based on the tree.
