Portland, OR, United States of America

Steven Burns


Average Co-Inventor Count = 6.0

ph-index = 2

Forward Citations = 23(Granted Patents)


Company Filing History:


Years Active: 2019-2022

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3 patents (USPTO):

Title: Innovations of Steven Burns

Introduction

Steven Burns is an accomplished inventor based in Portland, Oregon. He has made significant contributions to the field of technology, particularly in tensor processing. With a total of three patents to his name, Burns continues to push the boundaries of innovation.

Latest Patents

One of his latest patents is titled "Apparatus and method for adaptable and efficient lane-wise tensor processing." This invention focuses on performing efficient and adaptable tensor operations. In one embodiment, the processor includes front-end circuitry that schedules matrix operations in response to a matrix multiplication instruction. It features multiple lanes that execute matrix operations in parallel, with each lane containing an arithmetic logic unit designed to multiply blocks of matrices and accumulate results. Another patent under the same title further enhances this technology by introducing tile registers and tensor arithmetic logic units to optimize the multiplication and accumulation processes.

Career Highlights

Steven Burns is currently employed at Intel Corporation, where he applies his expertise in developing advanced processing technologies. His work has been instrumental in enhancing the efficiency of tensor operations, which are crucial for various applications in computing and artificial intelligence.

Collaborations

Throughout his career, Burns has collaborated with notable colleagues, including Jonathan D Pearce and David B Sheffield. These partnerships have fostered a creative environment that encourages innovation and the development of cutting-edge technologies.

Conclusion

Steven Burns exemplifies the spirit of innovation through his work at Intel Corporation and his contributions to tensor processing technology. His patents reflect a commitment to advancing the field and improving computational efficiency.

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