Patents by Inventor Swapnil P. Sakharshete

Swapnil P. Sakharshete 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).

  • Patent number: 11394396
    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: Grant
    Filed: September 25, 2020
    Date of Patent: July 19, 2022
    Assignee: Advanced Micro Devices, Inc.
    Inventors: Pramod Vasant Argade, Swapnil P. Sakharshete, Daniel N. Peroni
  • Publication number: 20220198739
    Abstract: A technique for performing ray tracing operations is provided. The technique includes performing bounding volume hierarchy (“BVH”) traversal in multiple accelerated processing devices (“APDs”), utilizing bounding volume hierarchy data copies in memories local to the multiple APDs; rendering primitives determined to be intersected based on the BVH traversal, using geometry information and texture data spread across the memories local to the multiple APDs; and storing results of rendered primitives for a set of tiles assigned to the multiple APDs into tile buffers stored in APD memories local to the APDs.
    Type: Application
    Filed: December 21, 2020
    Publication date: June 23, 2022
    Applicant: Advanced Micro Devices, Inc.
    Inventors: Skyler Jonathon Saleh, Maxim V. Kazakov, Swapnil P. Sakharshete, Takahiro Harada, Vineet Goel
  • Publication number: 20220101110
    Abstract: Techniques are disclosed for performing machine learning operations. The techniques include fetching weights for a first layer in a first format; performing matrix multiplication of the weights fetched in the first format with values provided by a prior layer in a forwards training pass; fetching the weights for the first layer in a second format different from the first format; and performing matrix multiplication for a backwards pass, the matrix multiplication including multiplication of the weights fetched in the second format with values corresponding to values provided as the result of the forwards training pass for the first layer.
    Type: Application
    Filed: September 25, 2020
    Publication date: March 31, 2022
    Applicant: Advanced Micro Devices, Inc.
    Inventors: Swapnil P. Sakharshete, Maxim V. Kazakov
  • Publication number: 20220103183
    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: September 25, 2020
    Publication date: March 31, 2022
    Inventors: Pramod Vasant Argade, Swapnil P. Sakharshete, Daniel N. Peroni
  • Publication number: 20210374607
    Abstract: A device is disclosed. The device includes a machine learning die including a memory and one or more machine learning accelerators; and a processing core die stacked with the machine learning die, the processing core die being configured to execute shader programs for controlling operations on the machine learning die, wherein the memory is configurable as either or both of a cache and a directly accessible memory.
    Type: Application
    Filed: December 21, 2020
    Publication date: December 2, 2021
    Applicant: Advanced Micro Devices, Inc.
    Inventors: Maxim V. Kazakov, Swapnil P. Sakharshete, Milind N. Nemlekar, Vineet Goel
  • Publication number: 20210150669
    Abstract: A processing device is provided which includes memory and a processor. The processor is configured to receive an input image having a first resolution, generate linear down-sampled versions of the input image by down-sampling the input image via a linear upscaling network and generate non-linear down-sampled versions of the input image by down-sampling the input image via a non-linear upscaling network.
    Type: Application
    Filed: November 18, 2019
    Publication date: May 20, 2021
    Applicant: Advanced Micro Devices, Inc.
    Inventors: Alexander M. Potapov, Skyler Jonathon Saleh, Swapnil P. Sakharshete, Vineet Goel
  • Publication number: 20210026686
    Abstract: Techniques for performing machine learning operations are provided. The techniques include configuring a first portion of a first chiplet as a cache; performing caching operations via the first portion; configuring at least a first sub-portion of the first portion of the chiplet as directly-accessible memory; and performing machine learning operations with the first sub-portion by a machine learning accelerator within the first chiplet.
    Type: Application
    Filed: July 20, 2020
    Publication date: January 28, 2021
    Applicant: Advanced Micro Devices, Inc.
    Inventors: Swapnil P. Sakharshete, Andrew S. Pomianowski, Maxim V. Kazakov, Vineet Goel, Milind N. Nemlekar, Skyler Jonathon Saleh
  • Publication number: 20200184002
    Abstract: A processing device is provided which includes memory configured to store data and a processor configured to determine, based on convolutional parameters associated with an image, a virtual general matrix-matrix multiplication (GEMM) space of a virtual GEMM space output matrix and generate, in the virtual GEMM space output matrix, a convolution result by matrix multiplying the data corresponding to a virtual GEMM space input matrix with the data corresponding to a virtual GEMM space filter matrix. The processing device also includes convolutional mapping hardware configured to map, based on the convolutional parameters, positions of the virtual GEMM space input matrix to positions of an image space of the image.
    Type: Application
    Filed: August 30, 2019
    Publication date: June 11, 2020
    Applicant: Advanced Micro Devices, Inc.
    Inventors: Swapnil P. Sakharshete, Samuel Lawrence Wasmundt, Maxim V. Kazakov, Vineet Goel