Company Filing History:
Years Active: 2022
Title: Inventor Xiaoqing Shan: Innovating with Neural Network Acceleration
Introduction: Xiaoqing Shan is a distinguished inventor based in Hong Kong, China, who has made significant strides in the field of neural network technology. With a focus on enhancing computational efficiency, Shan is recognized for his innovative contributions, particularly in dynamic tile configurations that optimize neural network performance.
Latest Patents: Xiaoqing Shan holds a patent for the "Dynamic Tile Parallel Neural Network Accelerator." This invention leverages a unique architecture that allows for reconfiguration of computational tiles based on the number and size needed for various applications. Each sub-array of computational cells features edge cells with an integrated vector multiplexer, enabling complex operations like Rectified Linear Unit (ReLU) calculations and pooling. The architecture is designed to shift outputs and weights efficiently between cells, which enhances the overall processing capabilities of the neural network.
Career Highlights: Shan's work primarily involves his position at the Hong Kong Applied Science and Technology Research Institute Company, Limited, where he contributes to pioneering research in technology and innovation. His experience and skills have solidified his role as a key figure in advancing neural network applications, making impactful contributions to the technology sector.
Collaborations: Xiaoqing Shan collaborates with talented professionals in his field, including Tao Li and Ka Lung Tim Wong. Together, they work on various projects that push the boundaries of current technology, fostering an environment of creativity and innovation.
Conclusion: Xiaoqing Shan exemplifies the spirit of innovation with his contributions to neural network technology. His patented dynamic tile parallel neural network accelerator highlights his ability to solve complex computational challenges. As he continues to collaborate and push the limits of technology, his work is likely to impact the future of artificial intelligence and machine learning.