Patents by Inventor Nicholas Malaya

Nicholas Malaya 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: 20220147668
    Abstract: Techniques are disclosed for compressing data. The techniques include identifying, in data to be compressed, a first set of values, wherein the first set of values include a first number of two or more consecutive identical non-zero values; including, in compressed data, a first control value indicating the first number of non-zero values and a first data item corresponding to the consecutive identical non-zero values; identifying, in the data to be compressed, a second value having an exponent value included in a defined set of exponent values; including, in the compressed data, a second control value indicating the exponent value and a second data item corresponding to a portion of the second value other than the exponent value; and including, in the compressed data, a third control value indicating a third set of one or more consecutive zero values in the data to be compressed.
    Type: Application
    Filed: November 10, 2020
    Publication date: May 12, 2022
    Applicant: Advanced Micro Devices, Inc.
    Inventors: Nicholas Malaya, Jakub Kurzak
  • Publication number: 20220027674
    Abstract: A generator for generating artificial data, and training for the same. Data corresponding to a first label is altered within a reference labeled data set. A discriminator is trained based on the reference labeled data set to create a selectively poisoned discriminator. A generator is trained based on the selectively poisoned discriminator to create a selectively poisoned generator. The selectively poisoned generator is tested for the first label and tested for the second label to determine whether the generator is sufficiently poisoned for the first label and sufficiently accurate for the second label. If it is not, the generator is retrained based on the data set including the further altered data. The generator includes a first ANN to input first information and output a set of artificial data that is classifiable using a first label and not classifiable using a second label of the set of labeled data.
    Type: Application
    Filed: August 9, 2021
    Publication date: January 27, 2022
    Applicant: Advanced Micro Devices, Inc.
    Inventor: Nicholas Malaya
  • Publication number: 20210383527
    Abstract: A technique for generating a trained discriminator is provided. The technique includes applying one or more of a glitched image or an unglitched image to a discriminator; receiving classification output from the discriminator; adjusting weights of the discriminator to improve classification accuracy of the discriminator; applying noise to a generator; receiving an output image from the generator; applying the output image to the discriminator to obtain a classification; and adjusting weights of one of the discriminator or the generator to improve ability of the generator to reduce classification accuracy of the discriminator, based on the classification.
    Type: Application
    Filed: September 23, 2020
    Publication date: December 9, 2021
    Applicants: Advanced Micro Devices, Inc., ATI Technologies ULC
    Inventors: Nicholas Malaya, Max Kiehn
  • Publication number: 20210383528
    Abstract: A technique for detecting a glitch in an image is provided. The technique includes providing an image to a plurality of individual classifiers to generate a plurality of individual classifier outputs and providing the plurality of individual classifier outputs to an ensemble classifier to generate a glitch classification.
    Type: Application
    Filed: September 23, 2020
    Publication date: December 9, 2021
    Applicants: Advanced Micro Devices, Inc., ATI Technologies ULC
    Inventors: Nicholas Malaya, Max Kiehn, Stanislav Ivashkevich
  • Patent number: 11087170
    Abstract: A generator for generating artificial data, and training for the same. Data corresponding to a first label is altered within a reference labeled data set. A discriminator is trained based on the reference labeled data set to create a selectively poisoned discriminator. A generator is trained based on the selectively poisoned discriminator to create a selectively poisoned generator. The selectively poisoned generator is tested for the first label and tested for the second label to determine whether the generator is sufficiently poisoned for the first label and sufficiently accurate for the second label. If it is not, the generator is retrained based on the data set including the further altered data. The generator includes a first ANN to input first information and output a set of artificial data that is classifiable using a first label and not classifiable using a second label of the set of labeled data.
    Type: Grant
    Filed: December 3, 2018
    Date of Patent: August 10, 2021
    Assignee: ADVANCED MICRO DEVICES, INC.
    Inventor: Nicholas Malaya
  • Publication number: 20210224130
    Abstract: Methods and systems for load balancing in a neural network system using metadata are disclosed. Any one or a combination of one or more kernels, one or more neurons, and one or more layers of the neural network system are tagged with metadata. A scheduler detects whether there are neurons that are available to execute. The scheduler uses the metadata to schedule and load balance computations across compute resources and available resources.
    Type: Application
    Filed: April 5, 2021
    Publication date: July 22, 2021
    Applicant: Advanced Micro Devices, Inc.
    Inventors: Nicholas Malaya, Yasuko Eckert
  • Publication number: 20210158222
    Abstract: Methods, devices, and systems for emulating a compute kernel with an ANN. The compute kernel is executed on a processor, and it is determined whether the compute kernel is a hotspot kernel. If the compute kernel is a hotspot kernel, the compute kernel is emulated with an ANN, and the ANN is substituted for the compute kernel.
    Type: Application
    Filed: November 25, 2019
    Publication date: May 27, 2021
    Applicant: Advanced Micro Devices, Inc.
    Inventor: Nicholas Malaya
  • Patent number: 10970120
    Abstract: Methods and systems for opportunistic load balancing in deep neural networks (DNNs) using metadata. Representative computational costs are captured, obtained or determined for a given architectural, functional or computational aspect of a DNN system. The representative computational costs are implemented as metadata for the given architectural, functional or computational aspect of the DNN system. In an implementation, the computed computational cost is implemented as the metadata. A scheduler detects whether there are neurons in subsequent layers that are ready to execute. The scheduler uses the metadata and neuron availability to schedule and load balance across compute resources and available resources.
    Type: Grant
    Filed: June 26, 2018
    Date of Patent: April 6, 2021
    Assignee: Advanced Micro Devices, Inc.
    Inventors: Nicholas Malaya, Yasuko Eckert
  • Publication number: 20200175329
    Abstract: A generator for generating artificial data, and training for the same. Data corresponding to a first label is altered within a reference labeled data set. A discriminator is trained based on the reference labeled data set to create a selectively poisoned discriminator. A generator is trained based on the selectively poisoned discriminator to create a selectively poisoned generator. The selectively poisoned generator is tested for the first label and tested for the second label to determine whether the generator is sufficiently poisoned for the first label and sufficiently accurate for the second label. If it is not, the generator is retrained based on the data set including the further altered data. The generator includes a first ANN to input first information and output a set of artificial data that is classifiable using a first label and not classifiable using a second label of the set of labeled data.
    Type: Application
    Filed: December 3, 2018
    Publication date: June 4, 2020
    Applicant: Advanced Micro Devices, Inc.
    Inventor: Nicholas Malaya
  • Publication number: 20190391850
    Abstract: Methods and systems for opportunistic load balancing in deep neural networks (DNNs) using metadata. Representative computational costs are captured, obtained or determined for a given architectural, functional or computational aspect of a DNN system. The representative computational costs are implemented as metadata for the given architectural, functional or computational aspect of the DNN system. In an implementation, the computed computational cost is implemented as the metadata. A scheduler detects whether there are neurons in subsequent layers that are ready to execute. The scheduler uses the metadata and neuron availability to schedule and load balance across compute resources and available resources.
    Type: Application
    Filed: June 26, 2018
    Publication date: December 26, 2019
    Applicant: Advanced Micro Devices, Inc.
    Inventors: Nicholas Malaya, Yasuko Eckert
  • Publication number: 20190188577
    Abstract: A system assigns experts of a mixture-of-experts artificial intelligence model to processing devices in an automated manner. The system includes an orchestrator component that maintains priority data that stores, for each of a set of experts, and for each of a set of execution parameters, ranking information that ranks different processing devices for the particular execution parameter. In one example, for the execution parameter of execution speed, and for a first expert, the priority data indicates that a central processing unit (“CPU”) executes the first expert faster than a graphics processing unit (“GPU”). In this example, for the execution parameter of power consumption, and for the first expert, the priority data indicates that a GPU uses less power than a CPU. The priority data stores such information for one or more processing devices, one or more experts, and one or more execution characteristics.
    Type: Application
    Filed: December 20, 2017
    Publication date: June 20, 2019
    Applicant: Advanced Micro Devices, Inc.
    Inventors: Nicholas Malaya, Nuwan Jayasena
  • Publication number: 20190005377
    Abstract: Training devices and methods for training an artificial neural network (ANN). The training device includes processing circuitry configured to transmit training data for the ANN and parameters for the ANN to an inference device. The processing circuitry is also configured to receive inference data, based on the training data and the parameters, from the inference device. The processing circuitry is also configured to receive inference timing information, based on the training data and the parameters, from the inference device. The processing circuitry is also configured to calculate a difference between the calculated inference data and expected inference data.
    Type: Application
    Filed: June 30, 2017
    Publication date: January 3, 2019
    Applicant: Advanced Micro Devices, Inc.
    Inventor: Nicholas Malaya