Patents by Inventor Evgenii ZHELTONOZHSKII

Evgenii ZHELTONOZHSKII 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).

  • System and method for emulating quantization noise for a neural network
    Patent number: 11972347
    Abstract: A system for training a quantized neural network dataset, comprising at least one hardware processor adapted to: receive input data comprising a plurality of training input value sets and a plurality of target value sets; in each of a plurality of training iterations: for each layer, comprising a plurality of weight values, of one or more of a plurality of layers of a neural network: compute a set of transformed values by applying to a plurality of layer values one or more emulated non-uniformly quantized transformations by adding to each of the plurality of layer values one or more uniformly distributed random noise values; and compute a plurality of output values; compute a plurality of training output values; and update one or more of the plurality of weight values to decrease a value of a loss function; and output the updated plurality of weight values of the plurality of layers.
    Type: Grant
    Filed: April 22, 2019
    Date of Patent: April 30, 2024
    Assignees: Technion Research & Development Foundation Limited, Ramot at Tel-Aviv University Ltd.
    Inventors: Chaim Baskin, Eliyahu Schwartz, Evgenii Zheltonozhskii, Alexander Bronstein, Natan Liss, Abraham Mendelson
  • SYSTEM AND METHOD FOR EMULATING QUANTIZATION NOISE FOR A NEURAL NETWORK
    Publication number: 20210241096
    Abstract: A system for training a quantized neural network dataset, comprising at least one hardware processor adapted to: receive input data comprising a plurality of training input value sets and a plurality of target value sets; in each of a plurality of training iterations: for each layer, comprising a plurality of weight values, of one or more of a plurality of layers of a neural network: compute a set of transformed values by applying to a plurality of layer values one or more emulated non-uniformly quantized transformations by adding to each of the plurality of layer values one or more uniformly distributed random noise values; and compute a plurality of output values; compute a plurality of training output values; and update one or more of the plurality of weight values to decrease a value of a loss function; and output the updated plurality of weight values of the plurality of layers.
    Type: Application
    Filed: April 22, 2019
    Publication date: August 5, 2021
    Applicants: Technion Research & Development Foundation Limited, Ramot at Tel-Aviv University Ltd.
    Inventors: Chaim BASKIN, Eliyahu SCHWARTZ, Evgenii ZHELTONOZHSKII, Alexander BRONSTEIN, Natan LISS, Abraham MENDELSON