Hillsboro, OR, United States of America

Vamsi Sripathi

This inventor holds 1 USPTO granted patent and 2 published patent applications. Top assignee: Intel Corporation. Active years: 2021.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Vamsi Sripathi. Retrieved from https://idiyas.com/inventor/vamsi-sripathi

Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile

USPTO Granted Patents = 1 

Average Co-Inventor Count = 8.0

ph-index = 1


Company Filing History:


Years Active: 2021

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1 patent (USPTO):Explore Patents

Title: Vamsi Sripathi: Innovator in Deep Learning Resource Management

Introduction

Vamsi Sripathi is an accomplished inventor based in Hillsboro, OR (US). He has made significant contributions to the field of deep learning and computing architectures. His innovative work focuses on optimizing resource usage for neural network workloads.

Latest Patents

Vamsi holds a patent titled "Automated resource usage configurations for deep learning neural network workloads on multi-generational computing architectures." This patent describes systems, apparatuses, and methods that identify different sets of compute nodes, optimizing their performance. The technology aims to minimize the completion time differences between slower and faster compute nodes when executing neural network workloads.

Career Highlights

Vamsi is currently employed at Intel Corporation, where he applies his expertise in deep learning and computing technologies. His work has been instrumental in advancing the efficiency of neural network processing.

Collaborations

Some of Vamsi's notable coworkers include Meenakshi Arunachalam and Kushal Datta. Their collaborative efforts contribute to the innovative environment at Intel Corporation.

Conclusion

Vamsi Sripathi is a key figure in the realm of deep learning innovations, with a focus on optimizing resource management in computing architectures. His contributions are paving the way for more efficient neural network processing.

Profile summary based on public USPTO records.
Data Sources: USPTO Patent Grant XML, Patent Center, EPO & CIPO • Normalized by IDiyas Innovation Graph. Methodology & provenance architecturePlease report any incorrect information to [email protected]
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