Patents by Inventor Maik RIECHERT

Maik RIECHERT 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).

  • NETWORK VERIFICATION SYSTEMS AND METHODS
    Publication number: 20230300053
    Abstract: A network verification system uses general-purpose programming language to create network verification tests. A test orchestrator builds a model of the network only using data from the network verification test. An optimization testing manager creates symbolic packets for verification tests using assertions based on a packet library embedded into the testing manager and the general-purpose programming language.
    Type: Application
    Filed: April 30, 2021
    Publication date: September 21, 2023
    Inventors: Ryan Andrew BECKETT, Karthick JAYARAMAN, Neha Milind RAJE, Jitendra PADHYE, Christopher Scott JOHNSTON, Steven Jeffrey BENALOH, Nikolaj BJORNER, Andrey Aleksandrovic RYBALCHENKO, Nuno CERQUEIRA AFONSO, Nuno CLAUDINO PEREIRA LOPES, Sharad AGARWAL, Hang Kwong LEE, Aniruddha PARKHI, Maik RIECHERT
  • ASYNCHRONOUS NEURAL NETWORK TRAINING
    Publication number: 20220222531
    Abstract: A neural network training apparatus is described which has a network of worker nodes each having a memory storing a subgraph of a neural network to be trained. The apparatus has a control node connected to the network of worker nodes. The control node is configured to send training data instances into the network to trigger parallelized message passing operations which implement a training algorithm which trains the neural network. At least some of the message passing operations asynchronously update parameters of individual subgraphs of the neural network at the individual worker nodes.
    Type: Application
    Filed: March 28, 2022
    Publication date: July 14, 2022
    Inventors: Ryota TOMIOKA, Matthew Alastair JOHNSON, Daniel Stefan TARLOW, Samuel Alexander WEBSTER, Dimitrios VYTINIOTIS, Alexander Lloyd GAUNT, Maik RIECHERT
  • Asynchronous neural network training
    Patent number: 11288575
    Abstract: A neural network training apparatus is described which has a network of worker nodes each having a memory storing a subgraph of a neural network to be trained. The apparatus has a control node connected to the network of worker nodes. The control node is configured to send training data instances into the network to trigger parallelized message passing operations which implement a training algorithm which trains the neural network. At least some of the message passing operations asynchronously update parameters of individual subgraphs of the neural network at the individual worker nodes.
    Type: Grant
    Filed: May 18, 2017
    Date of Patent: March 29, 2022
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Ryota Tomioka, Matthew Alastair Johnson, Daniel Stefan Tarlow, Samuel Alexander Webster, Dimitrios Vytiniotis, Alexander Lloyd Gaunt, Maik Riechert
  • Network verification systems and methods
    Patent number: 11121934
    Abstract: A network verification system uses general-purpose programming language to create network verification tests. A test orchestrator builds a model of the network only using data from the network verification test. An optimization testing manager creates symbolic packets for verification tests using assertions based on a packet library embedded into the testing manager and the general-purpose programming language.
    Type: Grant
    Filed: December 8, 2020
    Date of Patent: September 14, 2021
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Ryan Andrew Beckett, Karthick Jayaraman, Neha Milind Raje, Jitendra Padhye, Christopher Scott Johnston, Steven Jeffrey Benaloh, Nikolaj Bjorner, Andrey Aleksandrovic Rybalchenko, Nuno Cerqueira Afonso, Nuno Claudino Pereira Lopes, Sharad Agarwal, Hang Kwong Lee, Aniruddha Parkhi, Maik Riechert
  • ASYNCHRONOUS NEURAL NETWORK TRAINING
    Publication number: 20180336458
    Abstract: A neural network training apparatus is described which has a network of worker nodes each having a memory storing a subgraph of a neural network to be trained. The apparatus has a control node connected to the network of worker nodes. The control node is configured to send training data instances into the network to trigger parallelized message passing operations which implement a training algorithm which trains the neural network. At least some of the message passing operations asynchronously update parameters of individual subgraphs of the neural network at the individual worker nodes.
    Type: Application
    Filed: May 18, 2017
    Publication date: November 22, 2018
    Inventors: Ryota TOMIOKA, Matthew Alastair JOHNSON, Daniel Stefan TARLOW, Samuel Alexander WEBSTER, Dimitrios VYTINIOTIS, Alexander Lloyd GAUNT, Maik RIECHERT