Xiaoqiang Dan

Title: Innovations of Xiaoqiang Dan

Introduction

Xiaoqiang Dan is a prominent inventor based in Shenzhen, China. He has made significant contributions to the field of technology, particularly in the area of instruction generation and processing methods. With a total of two patents to his name, Dan continues to push the boundaries of innovation.

Latest Patents

Xiaoqiang Dan's latest patents include a "Single instruction multiple data SIMD instruction generation and processing method and related device." This invention provides a method for obtaining the length of each loop dimension of a first tensor formula and selecting information about a second SIMD instruction model that matches the first tensor formula. The method generates a first SIMD instruction based on the length of at least one loop dimension of the first tensor formula and the second SIMD instruction model.

Another notable patent is the "Intermediate representation construction method, compiler, and server." This method involves obtaining a first intermediate representation (IR) that includes a computing statement with a tensor and an operator. The method generates a second IR based on the computing statement, which includes first data migration information and data segmentation information.

Career Highlights

Xiaoqiang Dan is currently employed at Huawei Technologies Co., Limited, a leading global provider of information and communications technology (ICT) infrastructure and smart devices. His work at Huawei has allowed him to develop innovative solutions that enhance computing efficiency and performance.

Collaborations

Dan has collaborated with notable colleagues, including Chen Wu and Yifan Lin. These partnerships have fostered a creative environment that encourages the exchange of ideas and the development of groundbreaking technologies.

Conclusion

Xiaoqiang Dan's contributions to the field of technology through his patents and work at Huawei Technologies Co., Limited highlight his role as an influential inventor. His innovative methods in SIMD instruction generation and intermediate representation construction continue to shape the future of computing.