Patents by Inventor Igor Fedorov

Igor Fedorov 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: 20240046065
    Abstract: Example methods, apparatuses, and/or articles of manufacture are disclosed that may be implemented, in whole or in part, using one or more computing devices to determine options for decisions in connection with design features of a computing device. In a particular implementation, design options for two or more design decisions of neural network processing device may be identified based, at least in part, on combination of a definition of available computing resources and one or more predefined performance constraints.
    Type: Application
    Filed: August 3, 2022
    Publication date: February 8, 2024
    Inventors: Hokchhay Tann, Ramon Matas Navarro, Igor Fedorov, Chuteng Zhou, Paul Nicholas Whatmough, Matthew Mattina
  • Publication number: 20240020419
    Abstract: Methods and systems for detecting errors when performing a convolutional operation is provided. Predicted checksum data, corresponding to input checksum data and kernel checksum data, is obtained. The convolutional operation is performed to obtain an output feature map. Output checksum data is generated and the predicted checksum data and the output checksum data are compared, the comparing taking account of partial predicted checksum data configured to correct for a lack of padding when performing the convolution operation, wherein the partial predicted checksum data corresponds to input checksum data for a subset of the values in the input feature map and kernel checksum data for a subset of the values in the kernel.
    Type: Application
    Filed: July 15, 2022
    Publication date: January 18, 2024
    Inventors: Matthew David HADDON, Igor FEDOROV, Reiley JEYAPAUL, Paul Nicholas WHATMOUGH, Zhi-Gang LIU
  • Publication number: 20230229921
    Abstract: Neural network systems and methods are provided. One method for processing a neural network includes, for at least one neural network layer that includes a plurality of weights, applying an offset function to each of a plurality of weight values in the plurality of weights to generate an offset weight value, and quantizing the offset weight values to form quantized offset weight values. The plurality of weights are pruned. One method for executing a neural network includes reading, from a memory, at least one neural network layer that includes quantized offset weight values and an offset value ?, and performing a neural network layer operation on an input feature map, based on the quantized offset weight values and the offset value ?, to generate an output feature map. The quantized offset weight values are signed integer numbers.
    Type: Application
    Filed: January 14, 2022
    Publication date: July 20, 2023
    Applicant: Arm Limited
    Inventors: Igor Fedorov, Paul Nicholas Whatmough
  • Publication number: 20230042271
    Abstract: Example methods, apparatuses, and/or articles of manufacture are disclosed that may be implemented, in whole or in part, using one or more computing devices to select options for decisions in connection with design features of a computing device. In a particular implementation, design options for two or more design decisions of neural network processing device may be selected based, at least in part, on combination of function values that are computed based, at least in part, on a tensor expressing sample neural network weights.
    Type: Application
    Filed: August 4, 2021
    Publication date: February 9, 2023
    Inventors: Igor Fedorov, Ramon Matas Navarro, Chuteng Zhou, Hokchhay Tann, Paul Nicholas Whatmough, Matthew Mattina
  • Patent number: 10726525
    Abstract: An image denoising neural network training architecture includes an image denoising neural network and a clean data neural network, and the image denoising neural network and clean data neural network share information between each other.
    Type: Grant
    Filed: March 20, 2018
    Date of Patent: July 28, 2020
    Assignee: Samsung Electronics Co., Ltd.
    Inventors: Mostafa El-Khamy, Igor Fedorov, Jungwon Lee
  • Publication number: 20190096038
    Abstract: An image denoising neural network training architecture includes an image denoising neural network and a clean data neural network, and the image denoising neural network and clean data neural network share information between each other.
    Type: Application
    Filed: March 20, 2018
    Publication date: March 28, 2019
    Inventors: Mostafa El-Khamy, Igor Fedorov, Jungwon Lee