Patents by Inventor Michael Klachko

Michael Klachko 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).

  • SYSTEMS AND METHODS FOR ENHANCING INFERENTIAL ACCURACY OF AN ARTIFICIAL NEURAL NETWORK DURING TRAINING ON A MIXED-SIGNAL INTEGRATED CIRCUIT
    Publication number: 20230359874
    Abstract: A system and method for enhancing inferential accuracy of an artificial neural network during training includes during a simulated training of an artificial neural network identifying channel feedback values of a plurality of distinct channels of a layer of the artificial neural network based on an input of a training batch; if the channel feedback values do not satisfy a channel signal range threshold, computing a channel equalization factor based on the channel feedback values; identifying a layer feedback value based on the input of the training batch; and if the layer feedback value does not satisfy a layer signal range threshold, identifying a composite scaling factor based on the layer feedback values; during a non-simulated training of the artificial neural network, providing training inputs of: the training batch; the composite scaling factor; the channel equalization factor; and training the artificial neural network based on the training inputs.
    Type: Application
    Filed: July 15, 2023
    Publication date: November 9, 2023
    Inventors: David Fick, Daniel Graves, Michael Klachko, Ilya Perminov, John Mark Beardslee, Evgeny Shapiro
  • Systems and methods for enhancing inferential accuracy of an artificial neural network during training on a mixed-signal integrated circuit
    Patent number: 11720784
    Abstract: A system and method for enhancing inferential accuracy of an artificial neural network during training includes during a simulated training of an artificial neural network identifying channel feedback values of a plurality of distinct channels of a layer of the artificial neural network based on an input of a training batch; if the channel feedback values do not satisfy a channel signal range threshold, computing a channel equalization factor based on the channel feedback values; identifying a layer feedback value based on the input of the training batch; and if the layer feedback value does not satisfy a layer signal range threshold, identifying a composite scaling factor based on the layer feedback values; during a non-simulated training of the artificial neural network, providing training inputs of: the training batch; the composite scaling factor; the channel equalization factor; and training the artificial neural network based on the training inputs.
    Type: Grant
    Filed: March 25, 2022
    Date of Patent: August 8, 2023
    Assignee: Mythic, Inc.
    Inventors: David Fick, Daniel Graves, Michael Klachko, Ilya Perminov, John Mark Beardslee, Evgeny Shapiro
  • SYSTEMS AND METHODS FOR ENHANCING INFERENTIAL ACCURACY OF AN ARTIFICIAL NEURAL NETWORK DURING TRAINING ON A MIXED-SIGNAL INTEGRATED CIRCUIT
    Publication number: 20220318609
    Abstract: A system and method for enhancing inferential accuracy of an artificial neural network during training includes during a simulated training of an artificial neural network identifying channel feedback values of a plurality of distinct channels of a layer of the artificial neural network based on an input of a training batch; if the channel feedback values do not satisfy a channel signal range threshold, computing a channel equalization factor based on the channel feedback values; identifying a layer feedback value based on the input of the training batch; and if the layer feedback value does not satisfy a layer signal range threshold, identifying a composite scaling factor based on the layer feedback values; during a non-simulated training of the artificial neural network, providing training inputs of: the training batch; the composite scaling factor; the channel equalization factor; and training the artificial neural network based on the training inputs.
    Type: Application
    Filed: March 25, 2022
    Publication date: October 6, 2022
    Inventors: David Fick, Daniel Graves, Michael Klachko, Ilya Perminov, John Mark Beardslee, Evgeny Shapiro