Austin, TX, United States of America

Adam L Bordelon


Average Co-Inventor Count = 3.9

ph-index = 5

Forward Citations = 127(Granted Patents)


Company Filing History:


Years Active: 2013-2017

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6 patents (USPTO):Explore Patents

Title: Innovations of Adam L Bordelon

Introduction

Adam L Bordelon is a notable inventor based in Austin, TX, with a significant contribution to the field of data flow programming. He holds a total of six patents, showcasing his expertise and innovative spirit in developing advanced programming techniques.

Latest Patents

Among his latest patents is the "Loop parallelization analyzer for data flow programs." This invention focuses on automatically parallelizing iterative functionality in a data flow program. It involves storing a data flow program that includes an iterative portion and generating program code for multiple second data flow program portions. These portions are designed to execute iterations concurrently, ensuring that the execution remains functionally equivalent to the sequential execution of the original program.

Another significant patent is "Rearrangement of algebraic expressions based on operand ranking schemes." This system rearranges algebraic expressions in program code by scanning for specified expressions and computing ranks for each operand. The final expression is obtained through algebraic transformations, ensuring that operands are combined in a specific order based on their ranks.

Career Highlights

Adam L Bordelon is currently employed at National Instruments Corporation, where he continues to innovate and contribute to advancements in programming technologies. His work has had a profound impact on the efficiency and effectiveness of data flow programs.

Collaborations

He has collaborated with notable coworkers, including Haoran Yi and Robert E Dye, who have contributed to his projects and innovations.

Conclusion

Adam L Bordelon is a distinguished inventor whose work in data flow programming has led to significant advancements in the field. His patents reflect a deep understanding of programming complexities and a commitment to enhancing computational efficiency.

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