Patents by Inventor Azalia Mirhoseini

Azalia Mirhoseini 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).

  • Publication number: 20200364389
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a computer chip floorplan. One of the methods includes obtaining netlist data for a computer chip; and generating a computer chip floorplan, comprising placing a respective node at each time step in a sequence comprising a plurality of time steps, the placing comprising, for each time step: generating an input representation for the time step; processing the input representation using a node placement neural network having a plurality of network parameters, wherein the node placement neural network is configured to process the input representation in accordance with current values of the network parameters to generate a score distribution over a plurality of positions on the surface of the computer chip; and assigning the node to be placed at the time step to a position from the plurality of positions using the score distribution.
    Type: Application
    Filed: June 1, 2020
    Publication date: November 19, 2020
    Inventors: Chian-min Richard Ho, William Hang, Mustafa Nazim Yazgan, Anna Darling Goldie, Jeffrey Adgate Dean, Azalia Mirhoseini, Emre Tuncer, Ya Wang, Anand Babu
  • Publication number: 20200279150
    Abstract: A system includes a neural network that includes a Mixture of Experts (MoE) subnetwork between a first neural network layer and a second neural network layer. The MoE subnetwork includes multiple expert neural networks. Each expert neural network is configured to process a first layer output generated by the first neural network layer to generate a respective expert output. The MoE subnetwork further includes a gating subsystem that selects, based on the first layer output, one or more of the expert neural networks and determine a respective weight for each selected expert neural network, provides the first layer output as input to each of the selected expert neural networks, combines the expert outputs generated by the selected expert neural networks in accordance with the weights for the selected expert neural networks to generate an MoE output, and provides the MoE output as input to the second neural network layer.
    Type: Application
    Filed: May 20, 2020
    Publication date: September 3, 2020
    Inventors: Noam M. Shazeer, Azalia Mirhoseini, Krzysztof Stanislaw Maziarz
  • Publication number: 20200279163
    Abstract: A method for determining a placement for machine learning model operations across multiple hardware devices is described.
    Type: Application
    Filed: May 20, 2020
    Publication date: September 3, 2020
    Inventors: Samuel Bengio, Mohammad Norouzi, Benoit Steiner, Jeffrey Adgate Dean, Hieu Hy Pham, Azalia Mirhoseini, Quoc V. Le, Naveen Kumar, Yuefeng Zhou, Rasmus Munk Larsen
  • Patent number: 10719761
    Abstract: A system includes a neural network that includes a Mixture of Experts (MoE) subnetwork between a first neural network layer and a second neural network layer. The MoE subnetwork includes multiple expert neural networks. Each expert neural network is configured to process a first layer output generated by the first neural network layer to generate a respective expert output. The MoE subnetwork further includes a gating subsystem that selects, based on the first layer output, one or more of the expert neural networks and determine a respective weight for each selected expert neural network, provides the first layer output as input to each of the selected expert neural networks, combines the expert outputs generated by the selected expert neural networks in accordance with the weights for the selected expert neural networks to generate an MoE output, and provides the MoE output as input to the second neural network layer.
    Type: Grant
    Filed: April 24, 2019
    Date of Patent: July 21, 2020
    Assignee: Google LLC
    Inventors: Noam M. Shazeer, Azalia Mirhoseini, Krzysztof Stanislaw Maziarz
  • Patent number: 10699043
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a computer chip floorplan. One of the methods includes obtaining netlist data for a computer chip; and generating a computer chip floorplan, comprising placing a respective node at each time step in a sequence comprising a plurality of time steps, the placing comprising, for each time step: generating an input representation for the time step; processing the input representation using a node placement neural network having a plurality of network parameters, wherein the node placement neural network is configured to process the input representation in accordance with current values of the network parameters to generate a score distribution over a plurality of positions on the surface of the computer chip; and assigning the node to be placed at the time step to a position from the plurality of positions using the score distribution.
    Type: Grant
    Filed: December 4, 2019
    Date of Patent: June 30, 2020
    Assignee: Google LLC
    Inventors: Chian-min Richard Ho, William Hang, Mustafa Nazim Yazgan, Anna Darling Goldie, Jeffrey Adgate Dean, Azalia Mirhoseini, Emre Tuncer, Ya Wang, Anand Babu
  • Patent number: 10692003
    Abstract: A method for determining a placement for machine learning model operations across multiple hardware devices is described.
    Type: Grant
    Filed: June 19, 2019
    Date of Patent: June 23, 2020
    Assignee: Google LLC
    Inventors: Samuel Bengio, Mohammad Norouzi, Benoit Steiner, Jeffrey Adgate Dean, Hieu Hy Pham, Azalia Mirhoseini, Quoc V. Le, Naveen Kumar, Yuefeng Zhou, Rasmus Munk Larsen
  • Publication number: 20200175216
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a computer chip floorplan. One of the methods includes obtaining netlist data for a computer chip; and generating a computer chip floorplan, comprising placing a respective node at each time step in a sequence comprising a plurality of time steps, the placing comprising, for each time step: generating an input representation for the time step; processing the input representation using a node placement neural network having a plurality of network parameters, wherein the node placement neural network is configured to process the input representation in accordance with current values of the network parameters to generate a score distribution over a plurality of positions on the surface of the computer chip; and assigning the node to be placed at the time step to a position from the plurality of positions using the score distribution.
    Type: Application
    Filed: December 4, 2019
    Publication date: June 4, 2020
    Inventors: Chian-min Richard Ho, William Hang, Mustafa Nazim Yazgan, Anna Darling Goldie, Jeffrey Adgate Dean, Azalia Mirhoseini, Emre Tuncer, Ya Wang, Anand Babu
  • Publication number: 20190392294
    Abstract: A method for determining a placement for machine learning model operations across multiple hardware devices includes receiving data specifying machine learning operations, and determining a placement that assigns each of the operations specified by the data to a respective device from the multiple hardware devices.
    Type: Application
    Filed: August 28, 2019
    Publication date: December 26, 2019
    Inventors: Benoit Steiner, Anna Darling Goldie, Jeffrey Adgate Dean, Hieu Hy Pham, Azalia Mirhoseini, Quoc V. Le
  • Patent number: 10438113
    Abstract: A method for determining a placement for machine learning model operations across multiple hardware devices includes receiving data specifying machine learning operations, and determining a placement that assigns each of the operations specified by the data to a respective device from the multiple hardware devices.
    Type: Grant
    Filed: July 19, 2018
    Date of Patent: October 8, 2019
    Assignee: Google LLC
    Inventors: Benoit Steiner, Anna Darling Goldie, Jeffrey Adgate Dean, Hieu Hy Pham, Azalia Mirhoseini, Quoc V. Le
  • Publication number: 20190303761
    Abstract: A method for determining a placement for machine learning model operations across multiple hardware devices is described.
    Type: Application
    Filed: June 19, 2019
    Publication date: October 3, 2019
    Inventors: Samy Bengio, Mohammad Edward Norouzi, Benoit Steiner, Jeffrey Adgate Dean, Hieu Hy Pham, Azalia Mirhoseini, Quoc V. Le, Naveen Kumar, Yuefeng Zhou, Rasmus Munk Larsen
  • Publication number: 20190251423
    Abstract: A system includes a neural network that includes a Mixture of Experts (MoE) subnetwork between a first neural network layer and a second neural network layer. The MoE subnetwork includes multiple expert neural networks. Each expert neural network is configured to process a first layer output generated by the first neural network layer to generate a respective expert output. The MoE subnetwork further includes a gating subsystem that selects, based on the first layer output, one or more of the expert neural networks and determine a respective weight for each selected expert neural network, provides the first layer output as input to each of the selected expert neural networks, combines the expert outputs generated by the selected expert neural networks in accordance with the weights for the selected expert neural networks to generate an MoE output, and provides the MoE output as input to the second neural network layer.
    Type: Application
    Filed: April 24, 2019
    Publication date: August 15, 2019
    Inventors: Noam M. Shazeer, Azalia Mirhoseini, Krzysztof Stanislaw Maziarz
  • Publication number: 20190026624
    Abstract: A method for determining a placement for machine learning model operations across multiple hardware devices includes receiving data specifying machine learning operations, and determining a placement that assigns each of the operations specified by the data to a respective device from the multiple hardware devices.
    Type: Application
    Filed: July 19, 2018
    Publication date: January 24, 2019
    Inventors: Benoit Steiner, Anna Darling Goldie, Jeffrey Adgate Dean, Hieu Hy Pham, Azalia Mirhoseini, Quoc V. Le
  • Patent number: 9911088
    Abstract: A “Context-Aware Crowdsourced Task Optimizer” provides various processes to optimize task recommendations for workers in mobile crowdsourcing scenarios by automatically identifying and recommending bundles of tasks compatible with workers' contexts (e.g., worker history, present or expected locations, travel paths, working hours, skill sets, capabilities of worker's mobile computing devices, etc.). The Context-Aware Crowdsourced Task Optimizer bundles tasks to both maximize expected numbers of completed tasks and to dynamically price tasks to maximize the system's utility, which is a function of task values and task completion rates. Advantageously, the resulting task identification and recommendation process incentivizes individual workers to perform more tasks in a shorter time period, thereby helping tasks to complete faster, even with smaller budgets.
    Type: Grant
    Filed: May 1, 2014
    Date of Patent: March 6, 2018
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Suman Nath, Michel Goraczko, Jie Liu, Azalia Mirhoseini
  • Publication number: 20150317582
    Abstract: A “Context-Aware Crowdsourced Task Optimizer” provides various processes to optimize task recommendations for workers in mobile crowdsourcing scenarios by automatically identifying and recommending bundles of tasks compatible with workers' contexts (e.g., worker history, present or expected locations, travel paths, working hours, skill sets, capabilities of worker's mobile computing devices, etc.). The Context-Aware Crowdsourced Task Optimizer bundles tasks to both maximize expected numbers of completed tasks and to dynamically price tasks to maximize the system's utility, which is a function of task values and task completion rates. Advantageously, the resulting task identification and recommendation process incentivizes individual workers to perform more tasks in a shorter time period, thereby helping tasks to complete faster, even with smaller budgets.
    Type: Application
    Filed: May 1, 2014
    Publication date: November 5, 2015
    Applicant: Microsoft Corporation
    Inventors: Suman Nath, Michel Goraczko, Jie Liu, Azalia Mirhoseini