Austin, TX, United States of America

Alexander Dang-Tran

USPTO Granted Patents = 3 

Average Co-Inventor Count = 7.0

ph-index = 1

Forward Citations = 7(Granted Patents)


Company Filing History:


Years Active: 2021-2025

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3 patents (USPTO):Explore Patents

Title: Innovations of Alexander Dang-Tran

Introduction

Alexander Dang-Tran is an accomplished inventor based in Austin, TX. He has made significant contributions to the field of integrated circuit architecture, focusing on optimizing data flow for neural network applications. With a total of 3 patents, his work is at the forefront of technological advancements in this area.

Latest Patents

Among his latest patents, Alexander has developed systems and methods for intelligently buffering tracking for optimized data flow within integrated circuit architecture. This innovation includes creating a task graph based on transforming an optimized network graph for neural network applications. The process involves enumerating distinct tasks based on the decomposition of network operations and allocating data buffers to dependent tasks. Additionally, he has encoded a token-informed task scheduler to enhance the execution of these tasks by identifying the state of respective data buffers.

Career Highlights

Alexander is currently employed at Mythic, Inc., where he continues to push the boundaries of technology. His work is instrumental in developing solutions that improve the efficiency of data processing in integrated circuits. His expertise in this field has positioned him as a key player in the industry.

Collaborations

Some of his notable coworkers include Pei-Ci Wu and Andrew Morten, who collaborate with him on various projects at Mythic, Inc. Their combined efforts contribute to the innovative environment that fosters groundbreaking advancements.

Conclusion

Alexander Dang-Tran's contributions to the field of integrated circuit architecture are noteworthy. His patents reflect a commitment to enhancing data flow optimization, which is crucial for the advancement of neural network applications. His work continues to inspire innovation in the tech industry.

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