Patents by Inventor Joseph Leon CORKERY

Joseph Leon CORKERY 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).

  • Neural network processor using compression and decompression of activation data to reduce memory bandwidth utilization
    Patent number: 11909422
    Abstract: A deep neural network (“DNN”) module compresses and decompresses neuron-generated activation data to reduce the utilization of memory bus bandwidth. The compression unit receives an uncompressed chunk of data generated by a neuron in the DNN module. The compression unit generates a mask portion and a data portion of a compressed output chunk. The mask portion encodes the presence and location of the zero and non-zero bytes in the uncompressed chunk of data. The data portion stores truncated non-zero bytes from the uncompressed chunk of data. A decompression unit receives a compressed chunk of data from memory in the DNN processor or memory of an application host. The decompression unit decompresses the compressed chunk of data using the mask portion and the data portion.
    Type: Grant
    Filed: November 11, 2022
    Date of Patent: February 20, 2024
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Joseph Leon Corkery, Benjamin Eliot Lundell, Larry Marvin Wall, Chad Balling McBride, Amol Ashok Ambardekar, George Petre, Kent D. Cedola, Boris Bobrov
  • NEURAL NETWORK PROCESSOR USING COMPRESSION AND DECOMPRESSION OF ACTIVATION DATA TO REDUCE MEMORY BANDWIDTH UTILIZATION
    Publication number: 20230071352
    Abstract: A deep neural network (“DNN”) module can compress and decompress neuron-generated activation data to reduce the utilization of memory bus bandwidth. The compression unit can receive an uncompressed chunk of data generated by a neuron in the DNN module. The compression unit generates a mask portion and a data portion of a compressed output chunk. The mask portion encodes the presence and location of the zero and non-zero bytes in the uncompressed chunk of data. The data portion stores truncated non-zero bytes from the uncompressed chunk of data. A decompression unit can receive a compressed chunk of data from memory in the DNN processor or memory of an application host. The decompression unit decompresses the compressed chunk of data using the mask portion and the data portion. This can reduce memory bus utilization, allow a DNN module to complete processing operations more quickly, and reduce power consumption.
    Type: Application
    Filed: November 11, 2022
    Publication date: March 9, 2023
    Inventors: Joseph Leon CORKERY, Benjamin Eliot LUNDELL, Larry Marvin WALL, Chad Balling McBRIDE, Amol Ashok AMBARDEKAR, George PETRE, Kent D. CEDOLA, Boris BOBROV
  • Neural network processor using compression and decompression of activation data to reduce memory bandwidth utilization
    Patent number: 11528033
    Abstract: A deep neural network (“DNN”) module compresses and decompresses neuron-generated activation data to reduce the utilization of memory bus bandwidth. The compression unit receives an uncompressed chunk of data generated by a neuron in the DNN module. The compression unit generates a mask portion and a data portion of a compressed output chunk. The mask portion encodes the presence and location of the zero and non-zero bytes in the uncompressed chunk of data. The data portion stores truncated non-zero bytes from the uncompressed chunk of data. A decompression unit receives a compressed chunk of data from memory in the DNN processor or memory of an application host. The decompression unit decompresses the compressed chunk of data using the mask portion and the data portion.
    Type: Grant
    Filed: April 13, 2018
    Date of Patent: December 13, 2022
    Assignee: MICROSOFT TECHNOLOGY LICENSING, LLC
    Inventors: Joseph Leon Corkery, Benjamin Eliot Lundell, Larry Marvin Wall, Chad Balling McBride, Amol Ashok Ambardekar, George Petre, Kent D. Cedola, Boris Bobrov
  • NEURAL NETWORK PROCESSOR USING COMPRESSION AND DECOMPRESSION OF ACTIVATION DATA TO REDUCE MEMORY BANDWIDTH UTILIZATION
    Publication number: 20180300606
    Abstract: A deep neural network (“DNN”) module can compress and decompress neuron-generated activation data to reduce the utilization of memory bus bandwidth. The compression unit can receive an uncompressed chunk of data generated by a neuron in the DNN module. The compression unit generates a mask portion and a data portion of a compressed output chunk. The mask portion encodes the presence and location of the zero and non-zero bytes in the uncompressed chunk of data. The data portion stores truncated non-zero bytes from the uncompressed chunk of data. A decompression unit can receive a compressed chunk of data from memory in the DNN processor or memory of an application host. The decompression unit decompresses the compressed chunk of data using the mask portion and the data portion. This can reduce memory bus utilization, allow a DNN module to complete processing operations more quickly, and reduce power consumption.
    Type: Application
    Filed: April 13, 2018
    Publication date: October 18, 2018
    Inventors: Joseph Leon CORKERY, Benjamin Eliot LUNDELL, Larry Marvin WALL, Chad Balling McBRIDE, Amol Ashok AMBARDEKAR, George PETRE, Kent D. CEDOLA, Boris BOBROV