San Francisco, CA, United States of America

Da Li

This inventor holds 1 USPTO granted patent. Top assignee: Sony Corporation. Active years: 2018.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Da Li. Retrieved from https://idiyas.com/inventor/da-li-8z2ktmj9

Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile

Calculating rank

% Patents Active = 100.0

Average Co-Inventor Count = 3.0

ph-index = 1

Forward Citations = 7(Granted Patents)


Company Filing History:


Years Active: 2018

Loading Chart...
1 patent (USPTO):Explore Patents

Title: The Innovations of Da Li: Pioneering Convolution Neural Networks

Introduction: Da Li is an accomplished inventor based in San Francisco, CA. He has made significant contributions to the field of artificial intelligence, particularly in the training of convolution neural networks. His innovative approach leverages both CPU and GPU resources to enhance computational efficiency.

Latest Patents: Da Li holds a patent for "Learning convolution neural networks on heterogeneous CPU-GPU platform." This patent outlines methods for training convolution neural networks using both GPU and CPU. The HetNet and HybNet approaches are central to his invention. The HetNet approach divides batches into partitions, allowing the GPU and CPU to process separate batches. Meanwhile, the HybNet approach separates the layers of a convolution neural network for processing by the GPU and CPU.

Career Highlights: Da Li is currently employed at Sony Corporation, where he continues to push the boundaries of technology. His work has garnered attention for its innovative use of hardware resources in machine learning applications.

Collaborations: Da Li collaborates with talented individuals such as Ming-Chang Liu and Xun Xu, contributing to a dynamic and innovative work environment.

Conclusion: Da Li's contributions to convolution neural networks exemplify the intersection of technology and innovation. His work not only enhances computational efficiency but also paves the way for future advancements in artificial intelligence.

Profile summary based on public USPTO records.
Data Sources: USPTO Patent Grant XML, Patent Center, EPO & CIPO • Normalized by IDiyas Innovation Graph. Methodology & provenance architecturePlease report any incorrect information to [email protected]
Loading…