Chennai, India

Karthic P

USPTO Granted Patents = 2 

Average Co-Inventor Count = 5.5

ph-index = 1


Company Filing History:


Years Active: 2024-2025

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2 patents (USPTO):Explore Patents

Title: Innovations by Karthic P in Circuit Design

Introduction

Karthic P is an accomplished inventor based in Chennai, India. He has made significant contributions to the field of circuit design, holding 2 patents that leverage machine learning to optimize design processes. His work is instrumental in enhancing the efficiency and effectiveness of circuit implementations.

Latest Patents

Karthic's latest patents include innovative methods for satisfying circuit design constraints using a combination of machine learning models. In this patent, multiple classifier models are applied to features of a circuit design after processing the design through a first phase of an implementation flow. Each classifier model is associated with one of multiple directives, which are linked to a second phase of the implementation flow. The models return values indicative of the likelihood of improving a quality metric. Additionally, he has developed a patent focused on optimizing the use of computer resources in implementing circuit designs through machine learning. This involves selecting between single-process and multi-process implementation flows based on the classification of circuit design features.

Career Highlights

Karthic P is currently employed at Xilinx, Inc., where he continues to innovate in the field of circuit design. His work has been pivotal in advancing the integration of machine learning techniques into traditional design processes, making them more efficient and resource-effective.

Collaborations

Karthic collaborates with notable colleagues such as Srinivasan Dasasathyan and Paul D Kundarewich. Their combined expertise contributes to the innovative environment at Xilinx, Inc., fostering advancements in circuit design technology.

Conclusion

Karthic P's contributions to circuit design through his patents demonstrate the potential of machine learning in engineering applications. His work not only enhances design efficiency but also sets a precedent for future innovations in the field.

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