Patents by Inventor Linda L. Hurd

Linda L. Hurd 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: 20210279571
    Abstract: An apparatus to facilitate optimization of a neural network (NN) is disclosed. The apparatus includes optimization logic to define a NN topology having one or more macro layers, adjust the one or more macro layers to adapt to input and output components of the NN and train the NN based on the one or more macro layers.
    Type: Application
    Filed: February 17, 2021
    Publication date: September 9, 2021
    Applicant: Intel Corporation
    Inventors: Narayan Srinivasa, Joydeep Ray, Nicolas C. Galoppo Von Borries, Ben J. Ashbaugh, Prasoonkumar Surti, Feng Chen, Barath Lakshmanan, Elmoustapha Ould-Ahmed-Vall, Liwei Ma, Linda L. Hurd, Abhishek R. Appu, John C. Weast, Sara S. Baghsorkhi, Justin E. Gottschlich, Chandrasekaran Sakthivel, Farshad Akhbari, Dukhwan Kim, Altug Koker, Nadathur Rajagopalan Satish
  • Publication number: 20210241417
    Abstract: An apparatus to facilitate compute optimization is disclosed. The apparatus includes a plurality of processing units each comprising a plurality of execution units (EUs), wherein the plurality of EUs comprise a first EU type and a second EU type.
    Type: Application
    Filed: January 11, 2021
    Publication date: August 5, 2021
    Applicant: Intel Corporation
    Inventors: Prasoonkumar Surti, Narayan Srinivasa, Feng Chen, Joydeep Ray, Ben J. Ashbaugh, Nicolas C. Galoppo Von Borries, Eriko Nurvitadhi, Balaji Vembu, Tsung-Han Lin, Kamal Sinha, Rajkishore Barik, Sara S. Baghsorkhi, Justin E. Gottschlich, Altug Koker, Nadathur Rajagopalan Satish, Farshad Akhbari, Dukhwan Kim, Wenyin Fu, Travis T. Schluessler, Josh B. Mastronarde, Linda L. Hurd, John H. Feit, Jeffery S. Boles, Adam T. Lake, Karthik Vaidyanathan, Devan Burke, Subramaniam Maiyuran, Abhishek R. Appu
  • Patent number: 11080811
    Abstract: An apparatus to facilitate compute optimization is disclosed. The apparatus includes a mixed precision core to perform a mixed precision multi-dimensional matrix multiply and accumulate operation on 16-bit and/or 32 bit floating-point elements.
    Type: Grant
    Filed: June 19, 2019
    Date of Patent: August 3, 2021
    Assignee: Intel Corporation
    Inventors: Abhishek R. Appu, Altug Koker, Linda L. Hurd, Dukhwan Kim, Mike B. Macpherson, John C. Weast, Feng Chen, Farshad Akhbari, Narayan Srinivasa, Nadathur Rajagopalan Satish, Joydeep Ray, Ping T. Tang, Michael S. Strickland, Xiaoming Chen, Anbang Yao, Tatiana Shpeisman
  • Patent number: 11080813
    Abstract: An apparatus to facilitate compute optimization is disclosed. The apparatus includes a mixed precision core to perform a mixed precision multi-dimensional matrix multiply and accumulate operation on 8-bit and/or 32 bit signed or unsigned integer elements.
    Type: Grant
    Filed: September 26, 2019
    Date of Patent: August 3, 2021
    Assignee: Intel Corporation
    Inventors: Abhishek R. Appu, Altug Koker, Linda L. Hurd, Dukhwan Kim, Mike B. Macpherson, John C. Weast, Feng Chen, Farshad Akhbari, Narayan Srinivasa, Nadathur Rajagopalan Satish, Joydeep Ray, Ping T. Tang, Michael S. Strickland, Xiaoming Chen, Anbang Yao, Tatiana Shpeisman
  • Publication number: 20210201438
    Abstract: A mechanism is described for facilitating inference coordination and processing utilization for machine learning at autonomous machines. A method of embodiments, as described herein, includes detecting, at training time, information relating to one or more tasks to be performed according to a training dataset relating to a processor including a graphics processor. The method may further include analyzing the information to determine one or more portions of hardware relating to the processor capable of supporting the one or more tasks, and configuring the hardware to pre-select the one or more portions to perform the one or more tasks, while other portions of the hardware remain available for other tasks.
    Type: Application
    Filed: January 7, 2021
    Publication date: July 1, 2021
    Applicant: Intel Corporation
    Inventors: Abhishek R. Appu, Altug Koker, John C. Weast, Mike B. Macpherson, Linda L. Hurd, Sara S. Baghsorkhi, Justin E. Gottschlich, Prasoonkumar Surti, Chandrasekaran Sakthivel, Liwei Ma, Elmoustapha Ould-Ahmed-Vall, Kamal Sinha, Joydeep Ray, Balaji Vembu, Sanjeev Jahagirdar, Vasanth Ranganathan, DUKHWAN Kim
  • Patent number: 11049213
    Abstract: A mechanism is described for facilitating sharing of data and compression expansion of models at autonomous machines. A method of embodiments, as described herein, includes detecting a first processor processing information relating to a neural network at a first computing device, where the first processor comprises a first graphics processor and the first computing device comprises a first autonomous machine. The method further includes facilitating the first processor to store one or more portions of the information in a library at a database, where the one or more portions are accessible to a second processor of a computing device.
    Type: Grant
    Filed: November 26, 2019
    Date of Patent: June 29, 2021
    Assignee: INTEL CORPORATION
    Inventors: Abhishek R. Appu, Altug Koker, John C. Weast, Mike B. Macpherson, Dukhwan Kim, Linda L. Hurd, Sara S. Baghsorkhi, Justin E. Gottschlich, Prasoonkumar Surti, Chandrasekaran Sakthivel, Joydeep Ray
  • Patent number: 11010659
    Abstract: In an example, an apparatus comprises a compute engine comprising a high precision component and a low precision component; and logic, at least partially including hardware logic, to receive instructions in the compute engine; select at least one of the high precision component or the low precision component to execute the instructions; and apply a gate to at least one of the high precision component or the low precision component to execute the instructions. Other embodiments are also disclosed and claimed.
    Type: Grant
    Filed: April 24, 2017
    Date of Patent: May 18, 2021
    Assignee: INTEL CORPORATION
    Inventors: Kamal Sinha, Balaji Vembu, Eriko Nurvitadhi, Nicolas C. Galoppo Von Borries, Rajkishore Barik, Tsung-Han Lin, Joydeep Ray, Ping T. Tang, Michael S. Strickland, Xiaoming Chen, Anbang Yao, Tatiana Shpeisman, Abhishek R. Appu, Altug Koker, Farshad Akhbari, Narayan Srinivasa, Feng Chen, Dukhwan Kim, Nadathur Rajagopalan Satish, John C. Weast, Mike B. MacPherson, Linda L. Hurd, Vasanth Ranganathan, Sanjeev S. Jahagirdar
  • Patent number: 11010953
    Abstract: Briefly, in accordance with one or more embodiments, a processor receives an incoming data stream that includes alpha channel data, and a memory stores an application programming interface (API). The API is to route the alpha channel data to a fixed point blending unit to perform one or more blending operations using fixed point representation of the alpha channel data. The API is further to route the incoming data stream to a floating point blending unit to perform operations involving floating point representation of the incoming data.
    Type: Grant
    Filed: April 21, 2017
    Date of Patent: May 18, 2021
    Assignee: Intel Corporation
    Inventors: Abhishek R. Appu, Prasoonkumar Surti, Srivallaba Mysore, Subhajit Dasgupta, Hiroshi Akiba, Eric J. Hoekstra, Linda L. Hurd, Travis T. Schluessler, Daren J. Schmidt
  • Patent number: 11010163
    Abstract: Disclosed herein is an apparatus which comprises a plurality of execution units, and a first general register file (GRF) communicatively couple to the plurality of execution units, wherein the first GRF is shared by the plurality of execution units.
    Type: Grant
    Filed: July 30, 2019
    Date of Patent: May 18, 2021
    Assignee: INTEL CORPORATION
    Inventors: Abhishek R. Appu, Altug Koker, Joydeep Ray, Kamal Sinha, Kiran C. Veernapu, Subramaniam Maiyuran, Prasoonkumar Surti, Guei-Yuan Lueh, David Puffer, Supratim Pal, Eric J. Hoekstra, Travis T. Schluessler, Linda L. Hurd
  • Patent number: 10929947
    Abstract: An embodiment of a graphics apparatus may include a context engine to determine contextual information, a recommendation engine communicatively coupled to the context engine to determine a recommendation based on the contextual information, and a configuration engine communicatively coupled to the recommendation engine to adjust a configuration of a graphics operation based on the recommendation. Other embodiments are disclosed and claimed.
    Type: Grant
    Filed: September 16, 2019
    Date of Patent: February 23, 2021
    Assignee: Intel Corporation
    Inventors: Joydeep Ray, Ankur N. Shah, Abhishek R. Appu, Deepak S. Vembar, ElMoustapha Ould-Ahmed-Vall, Atsuo Kuwahara, Travis T. Schluessler, Linda L. Hurd, Josh B. Mastronarde, Vasanth Ranganathan
  • Patent number: 10929749
    Abstract: An apparatus to facilitate optimization of a neural network (NN) is disclosed. The apparatus includes optimization logic to define a NN topology having one or more macro layers, adjust the one or more macro layers to adapt to input and output components of the NN and train the NN based on the one or more macro layers.
    Type: Grant
    Filed: April 24, 2017
    Date of Patent: February 23, 2021
    Assignee: INTEL CORPORATION
    Inventors: Narayan Srinivasa, Joydeep Ray, Nicolas C. Galoppo Von Borries, Ben Ashbaugh, Prasoonkumar Surti, Feng Chen, Barath Lakshmanan, Elmoustapha Ould-Ahmed-Vall, Liwei Ma, Linda L. Hurd, Abhishek R. Appu, John C. Weast, Sara S. Baghsorkhi, Justin E. Gottschlich, Chandrasekaran Sakthivel, Farshad Akhbari, Dukhwan Kim, Altug Koker, Nadathur Rajagopalan Satish
  • Publication number: 20210035257
    Abstract: A control surface tracks an individual cacheline in the original surface for frequent data values. If so, control surface bits are set. When reading a cacheline from memory, first the control surface bits are read. If they happen to be set, then the original memory read is skipped altogether and instead the bits from the control surface provide the value for the entire cacheline.
    Type: Application
    Filed: August 5, 2020
    Publication date: February 4, 2021
    Inventors: Saurabh Sharma, Abhishek Venkatesh, Travis T. Schluessler, Prasoonkumar Surti, Altug Koker, Aravindh V. Anantaraman, Pattabhiraman P. K., Abhishek R. Appu, Joydeep Ray, Kamal Sinha, Vasanth Ranganathan, Bhushan M. Borole, Wenyin Fu, Eric J. Hoekstra, Linda L. Hurd
  • Patent number: 10902547
    Abstract: An apparatus to facilitate compute optimization is disclosed. The apparatus includes a plurality of processing units each comprising a plurality of execution units (EUs), wherein the plurality of EUs comprise a first EU type and a second EU type.
    Type: Grant
    Filed: November 21, 2017
    Date of Patent: January 26, 2021
    Assignee: Intel Corporation
    Inventors: Prasoonkumar Surti, Narayan Srinivasa, Feng Chen, Joydeep Ray, Ben J. Ashbaugh, Nicolas C. Galoppo Von Borries, Eriko Nurvitadhi, Balaji Vembu, Tsung-Han Lin, Kamal Sinha, Rajkishore Barik, Sara S. Baghsorkhi, Justin E. Gottschlich, Altug Koker, Nadathur Rajagopalan Satish, Farshad Akhbari, Dukhwan Kim, Wenyin Fu, Travis T. Schluessler, Josh B. Mastronarde, Linda L. Hurd, John H. Feit, Jeffery S. Boles, Adam T. Lake, Karthik Vaidyanathan, Devan Burke, Subramaniam Maiyuran, Abhishek R. Appu
  • Patent number: 10891707
    Abstract: A mechanism is described for facilitating inference coordination and processing utilization for machine learning at autonomous machines. A method of embodiments, as described herein, includes detecting, at training time, information relating to one or more tasks to be performed according to a training dataset relating to a processor including a graphics processor. The method may further include analyzing the information to determine one or more portions of hardware relating to the processor capable of supporting the one or more tasks, and configuring the hardware to pre-select the one or more portions to perform the one or more tasks, while other portions of the hardware remain available for other tasks.
    Type: Grant
    Filed: April 8, 2019
    Date of Patent: January 12, 2021
    Assignee: Intel Corporation
    Inventors: Abhishek R. Appu, Altug Koker, John C. Weast, Mike B. Macpherson, Linda L. Hurd, Sara S. Baghsorkhi, Justin E. Gottschlich, Prasoonkumar Surti, Chandrasekaran Sakthivel, Liwei Ma, Elmoustapha Ould-Ahmed-Vall, Kamal Sinha, Joydeep Ray, Balaji Vembu, Sanjeev Jahagirdar, Vasanth Ranganathan, Dukhwan Kim
  • Patent number: 10866590
    Abstract: Apparatuses, methods, and systems associated with safety-related decision making reporting and regulation of computer-assisted or autonomous driving (CA/AD) vehicles are disclosed herein. In some embodiments, an apparatus includes a safety-related decision making reporting unit, disposed in a CA/AD vehicle, to collect data about driving behavior of the CA/AD vehicle and to determine whether the collected data is related to a safety-related decision making rule. In embodiments, the collected data is to be reported to a remote organization associated with regulating the safety-related decision making rule. In some embodiments, a computing device or server associated with regulating safety-related decision making rules receives the collected data from the CA/AD vehicle and/or manufacturers of the CA/AD vehicle. In embodiments, the computing device analyzes the collected data to modify or generate a safety-decision making rule. Other embodiments are also described and claimed.
    Type: Grant
    Filed: September 28, 2018
    Date of Patent: December 15, 2020
    Assignee: Intel Corporation
    Inventors: Xue Yang, Sherry Chang, Chaitanya Sreerama, Linda L. Hurd, Denica Larsen
  • Patent number: 10853906
    Abstract: One embodiment provides an accelerator module comprising a memory stack including multiple memory dies; a graphics processing unit (GPU) coupled with the memory stack via one or more memory controllers, the GPU including a plurality of multiprocessors having a single instruction, multiple thread (SIMT) architecture, the multiprocessors to execute at least one single instruction. The at least one single instruction is to cause at least a portion of the GPU to perform a floating point operation on input having differing precisions. The floating point operation is a two-dimensional matrix multiply and accumulate operation.
    Type: Grant
    Filed: November 21, 2018
    Date of Patent: December 1, 2020
    Assignee: Intel Corporation
    Inventors: Elmoustapha Ould-Ahmed-Vall, Sara S. Baghsorkhi, Anbang Yao, Kevin Nealis, Xiaoming Chen, Altug Koker, Abhishek R. Appu, John C. Weast, Mike B. Macpherson, Dukhwan Kim, Linda L. Hurd, Ben J. Ashbaugh, Barath Lakshmanan, Liwei Ma, Joydeep Ray, Ping T. Tang, Michael S. Strickland
  • Publication number: 20200364822
    Abstract: An apparatus to facilitate compute optimization is disclosed. The apparatus includes a mixed precision core to perform a mixed precision multi-dimensional matrix multiply and accumulate operation on 8-bit and/or 32 bit signed or unsigned integer elements.
    Type: Application
    Filed: August 3, 2020
    Publication date: November 19, 2020
    Applicant: Intel Corporation
    Inventors: Abhishek R. Appu, Altug Koker, Linda L. Hurd, Dukhwan Kim, Mike B. Macpherson, John C. Weast, Feng Chen, Farshad Akhbari, Narayan Srinivasa, Nadathur Rajagopalan Satish, Joydeep Ray, Ping T. Tang, Michael S. Strickland, Xiaoming Chen, Anbang Yao, Tatiana Shpeisman
  • Publication number: 20200364823
    Abstract: Embodiments described herein provide a graphics processor that can perform a variety of mixed and multiple precision instructions and operations. One embodiment provides a streaming multiprocessor that can concurrently execute multiple thread groups, wherein the streaming multiprocessor includes a single instruction, multiple thread (SIMT) architecture and the streaming multiprocessor is to execute multiple threads for each of multiple instructions. The streaming multiprocessor can perform concurrent integer and floating-point operations and includes a mixed precision core to perform operations at multiple precisions.
    Type: Application
    Filed: August 3, 2020
    Publication date: November 19, 2020
    Applicant: Intel Corporation
    Inventors: Elmoustapha Ould-Ahmed-Vall, Sara S. Baghsorkhi, Anbang Yao, Kevin Nealis, Xiaoming Chen, Altug Koker, Abhishek R. Appu, John C. Weast, Mike B. Macpherson, Dukhwan Kim, Linda L. Hurd, Ben J. Ashbaugh, Barath Lakshmanan, Liwei Ma, Joydeep Ray, Ping T. Tang, Michael S. Strickland
  • Patent number: 10748238
    Abstract: A control surface tracks an individual cacheline in the original surface for frequent data values. If so, control surface bits are set. When reading a cacheline from memory, first the control surface bits are read. If they happen to be set, then the original memory read is skipped altogether and instead the bits from the control surface provide the value for the entire cacheline.
    Type: Grant
    Filed: February 19, 2019
    Date of Patent: August 18, 2020
    Assignee: Intel Corporation
    Inventors: Saurabh Sharma, Abhishek Venkatesh, Travis T. Schluessler, Prasoonkumar Surti, Altug Koker, Aravindh V. Anantaraman, Pattabhiraman P. K., Abhishek R. Appu, Joydeep Ray, Kamal Sinha, Vasanth Ranganathan, Bhushan M. Borole, Wenyin Fu, Eric J. Hoekstra, Linda L. Hurd
  • Patent number: 10726514
    Abstract: One embodiment provides a general-purpose graphics processing unit comprising a dynamic precision floating-point unit including a control unit having precision tracking hardware logic to track an available number of bits of precision for computed data relative to a target precision, wherein the dynamic precision floating-point unit includes computational logic to output data at multiple precisions.
    Type: Grant
    Filed: April 28, 2017
    Date of Patent: July 28, 2020
    Assignee: Intel Corporation
    Inventors: Elmoustapha Ould-Ahmed-Vall, Sara S. Baghsorkhi, Anbang Yao, Kevin Nealis, Xiaoming Chen, Altug Koker, Abhishek R. Appu, John C. Weast, Mike B. Macpherson, Dukhwan Kim, Linda L. Hurd, Ben J. Ashbaugh, Barath Lakshmanan, Liwei Ma, Joydeep Ray, Ping T. Tang, Michael S. Strickland