International business machines corporation (20240135083). REINFORCEMENT LEARNING BASED CORRECTION OF TIMING FAILURES simplified abstract

REINFORCEMENT LEARNING BASED CORRECTION OF TIMING FAILURES

Organization Name

international business machines corporation

Inventor(s)

Gregor Boronowsky of Böblingen (DE)

Marvin Von Der Ehe of Vellmar (DE)

Manuel Beck of Dettingen unter Teck (DE)

Jan Niklas Stegmaier of Sindelfingen (DE)

Simon Hermann Friedmann of Böblingen (DE)

REINFORCEMENT LEARNING BASED CORRECTION OF TIMING FAILURES - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240135083 titled 'REINFORCEMENT LEARNING BASED CORRECTION OF TIMING FAILURES

Simplified Explanation

The abstract describes a computer-implemented method of correcting timing failures in an integrated circuit design using a reinforcement learning agent with a neural network.

• The method involves receiving a graph encoding the network of conductors and repowering structures, and receiving modification recommendations from the reinforcement learning agent based on the input graph.

Potential Applications

• This technology could be applied in the semiconductor industry for improving the performance and reliability of integrated circuits.
• It could also be used in the field of electronic design automation to optimize circuit designs and reduce timing failures.

Problems Solved

• This technology addresses timing failures in integrated circuit designs, which can lead to performance issues and malfunctions.
• It provides a systematic approach to correcting timing failures using a reinforcement learning agent, which can improve the efficiency of the design process.

Benefits

• Improved performance and reliability of integrated circuits.
• Enhanced design optimization and faster time-to-market for electronic products.

Potential Commercial Applications

Optimizing Integrated Circuit Designs with Reinforcement Learning Agent

Possible Prior Art

There may be prior art related to using machine learning algorithms for optimizing circuit designs, but specific examples would need to be researched further.

What is the impact of this technology on the semiconductor industry?

This technology could revolutionize the semiconductor industry by providing a more efficient and effective way to correct timing failures in integrated circuit designs, ultimately leading to improved performance and reliability of electronic devices.

How does this method compare to traditional approaches in electronic design automation?

This method offers a more automated and intelligent solution to correcting timing failures compared to traditional approaches in electronic design automation, which may rely more on manual intervention and trial-and-error methods.

Original Abstract Submitted

disclosed herein is a computer implemented method of correcting a timing failure of a network of conductors and repowering structures in an integrated circuit design using a reinforcement learning agent. the reinforcement learning agent comprises a neural network. the method comprises: receiving a graph comprising nodes and edges that encodes said network of conductors and repowering structures; and receiving a modification recommendation from said reinforcement learning agent in response to inputting said graph into said reinforcement learning agent.