Company Filing History:
Years Active: 2022-2025
Title: The Innovative Contributions of Chien-Chun Chou
Introduction
Chien-Chun Chou, a distinguished inventor based in Morgan Hill, California, holds a remarkable portfolio of seven patents. His work primarily focuses on advancements in machine learning, pushing the boundaries of what's possible in this rapidly evolving field.
Latest Patents
Among his latest contributions, one notable patent is the "Method and system for memory management within a machine learning inference engine." This innovative method involves receiving a machine learning model in high-level code and generating an internal representation mapped to components in a multi-processing tile device. It efficiently allocates memory address ranges within processing tiles and compiles low-level instructions based on the linking of these memory spaces.
Another significant patent is titled "Method and apparatus for ML graphs by a compiler." This system and method disclose a way to split a machine learning graph. The compiler generates an internal representation of the ML model, partitioning the graph into subgraphs associated with different hardware components or processors, subsequently generating the necessary low-level instructions for execution.
Career Highlights
Chien-Chun Chou currently works for Marvell Asia Pte., Ltd., where he continues to innovate in the realm of machine learning technologies. His extensive experience and deep understanding of both theoretical and practical aspects of machine learning have made him a valuable asset to his company.
Collaborations
Throughout his career, Chien-Chun has collaborated with esteemed colleagues, including Ulf Hanebutte and Senad Durakovic. These partnerships have allowed for the exchange of ideas and have furthered advancements in the machine learning domain.
Conclusion
Chien-Chun Chou's contributions to the field of machine learning through his inventive patents significantly impact technological advancements. His innovative methods and systems reflect his commitment to enhancing memory management and graph processing, propelling the future of machine learning forward.