This inventor holds 50 USPTO granted patents and 1 published patent application and 1 EPO patent, primarily in Neural Network Acceleration. Top assignees: Amazon Technologies, Inc., Freescale Semiconductor,inc., Qualcomm Incorporated. Active years: 2011-2026.
Location History:
- Redwood City, CA (US) (2019)
- Austin, TX (US) (2011 - 2024)
Company Filing History:
28 out of 23,558 
15 out of 5,491 
3 out of 44,618 
Years Active: 2011-2026
Areas of Expertise:
Title: Innovator Spotlight: Kun Xu - Revolutionizing Direct Memory Access Technology
Introduction: Kun Xu, a prolific inventor based in Austin, TX, has made significant contributions to the field of direct memory access (DMA) technology. With an impressive portfolio of 34 patents, Xu is known for his innovative approaches to optimizing data transfers and reducing overhead in DMA engines.
Latest Patents: Xu's latest patents showcase his expertise in multidimensional and multiblock tensorized direct memory access descriptors. By utilizing striding configurations and tensorized memory descriptors, these techniques aim to enhance data transfer efficiency in DMA operations, particularly for neural network accelerators.
Career Highlights: Throughout his career, Kun Xu has garnered experience working with renowned companies such as Freescale Semiconductor and Amazon Technologies. His tenure at these establishments has provided him with valuable insights and opportunities to bring his groundbreaking ideas to fruition.
Collaborations: Xu has collaborated closely with esteemed colleagues including David B. Kramer and Ron Diamant. Together, they have spearheaded innovative projects and propelled advancements in DMA technology, reaffirming Xu's commitment to excellence and collaboration in his field.
Conclusion: Kun Xu's relentless pursuit of excellence and continuous drive for innovation have solidified his reputation as a trailblazer in the realm of DMA technology. With his remarkable patents and extensive industry experience, Xu continues to push the boundaries of technological advancement, shaping the future of data transfer efficiency.