Patents by Inventor Daniel William Hurt

Daniel William Hurt has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • ASYNCHRONOUS DISTRIBUTED DATA FLOW FOR MACHINE LEARNING WORKLOADS
    Publication number: 20250053444
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for distributing machine learning workloads, e.g., computations for training a neural network or computing an inference using a neural network, across multiple hardware accelerators.
    Type: Application
    Filed: August 23, 2024
    Publication date: February 13, 2025
    Inventors: Jeffrey Adgate Dean, Sudip Roy, Michael Acheson Isard, Aakanksha Chowdhery, Brennan Saeta, Chandramohan Amyangot Thekkath, Daniel William Hurt, Hyeontaek Lim, Laurent El Shafey, Parker Edward Schuh, Paul Ronald Barham, Ruoming Pang, Ryan Sepassi, Sanjay Ghemawat, Yonghui Wu
  • Asynchronous distributed data flow for machine learning workloads
    Patent number: 12112198
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for distributing machine learning workloads, e.g., computations for training a neural network or computing an inference using a neural network, across multiple hardware accelerators.
    Type: Grant
    Filed: December 15, 2022
    Date of Patent: October 8, 2024
    Assignee: Google LLC
    Inventors: Jeffrey Adgate Dean, Sudip Roy, Michael Acheson Isard, Aakanksha Chowdhery, Brennan Saeta, Chandramohan Amyangot Thekkath, Daniel William Hurt, Hyeontaek Lim, Laurent El Shafey, Parker Edward Schuh, Paul Ronald Barham, Ruoming Pang, Ryan Sepassi, Sanjay Ghemawat, Yonghui Wu
  • ASYNCHRONOUS DISTRIBUTED DATA FLOW FOR MACHINE LEARNING WORKLOADS
    Publication number: 20230118303
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for distributing machine learning workloads, e.g., computations for training a neural network or computing an inference using a neural network, across multiple hardware accelerators.
    Type: Application
    Filed: December 15, 2022
    Publication date: April 20, 2023
    Inventors: Jeffrey Adgate Dean, Sudip Roy, Michael Acheson Isard, Aakanksha Chowdhery, Brennan Saeta, Chandramohan Amyangot Thekkath, Daniel William Hurt, Hyeontaek Lim, Laurent El Shafey, Parker Edward Schuh, Paul Ronald Barham, Ruoming Pang, Ryan Sepassi, Sanjay Ghemawat, Yonghui Wu
  • Asynchronous distributed data flow for machine learning workloads
    Patent number: 11556381
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for distributing machine learning workloads, e.g., computations for training a neural network or computing an inference using a neural network, across multiple hardware accelerators.
    Type: Grant
    Filed: May 6, 2022
    Date of Patent: January 17, 2023
    Assignee: Google LLC
    Inventors: Jeffrey Adgate Dean, Sudip Roy, Michael Acheson Isard, Aakanksha Chowdhery, Brennan Saeta, Chandramohan Amyangot Thekkath, Daniel William Hurt, Hyeontaek Lim, Laurent El Shafey, Parker Edward Schuh, Paul Ronald Barham, Ruoming Pang, Ryan Sepassi, Sanjay Ghemawat, Yonghui Wu
  • ASYNCHRONOUS DISTRIBUTED DATA FLOW FOR MACHINE LEARNING WORKLOADS
    Publication number: 20220357985
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for distributing machine learning workloads, e.g., computations for training a neural network or computing an inference using a neural network, across multiple hardware accelerators.
    Type: Application
    Filed: May 6, 2022
    Publication date: November 10, 2022
    Inventors: Jeffrey Adgate Dean, Sudip Roy, Michael Acheson Isard, Aakanksha Chowdhery, Brennan Saeta, Chandramohan Amyangot Thekkath, Daniel William Hurt, Hyeontaek Lim, Laurent El Shafey, Parker Edward Schuh, Paul Ronald Barham, Ruoming Pang, Ryan Sepassi, Sanjay Ghemawat, Yonghui Wu