Patents by Inventor Kamal Sinha
Kamal Sinha 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).
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Publication number: 20240086138Abstract: In accordance with some embodiments, the render rate is varied across and/or up and down the display screen. This may be done based on where the user is looking in order to reduce power consumption and/or increase performance. Specifically the screen display is separated into regions, such as quadrants. Each of these regions is rendered at a rate determined by at least one of what the user is currently looking at, what the user has looked at in the past and/or what it is predicted that the user will look at next. Areas of less focus may be rendered at a lower rate, reducing power consumption in some embodiments.Type: ApplicationFiled: September 26, 2023Publication date: March 14, 2024Inventors: Eric J. Asperheim, Subramaniam Maiyuran, Kiran C. Veernapu, Sanjeev S. Jahagirdar, Balaji Vembu, Devan Burke, Philip R. Laws, Kamal Sinha, Abhishek R. Appu, Elmoustapha Ould-Ahmed-Vall, Peter L. Doyle, Joydeep Ray, Travis T. Schluessler, John H. Feit, Nikos Kaburlasos, Jacek Kwiatkowski, Altug Koker
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Publication number: 20240078629Abstract: Techniques to improve performance of matrix multiply operations are described in which a compute kernel can specify one or more element-wise operations to perform on output of the compute kernel before the output is transferred to higher levels of a processor memory hierarchy.Type: ApplicationFiled: September 14, 2023Publication date: March 7, 2024Applicant: Intel CorporationInventors: Eriko Nurvitadhi, Balaji Vembu, Tsung-Han Lin, Kamal Sinha, Rajkishore Barik, Nicolas C. Galoppo Von Borries
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Patent number: 11922535Abstract: Embodiments provide mechanisms to facilitate compute operations for deep neural networks. One embodiment comprises a graphics processing unit comprising one or more multiprocessors, at least one of the one or more multiprocessors including a register file to store a plurality of different types of operands and a plurality of processing cores. The plurality of processing cores includes a first set of processing cores of a first type and a second set of processing cores of a second type. The first set of processing cores are associated with a first memory channel and the second set of processing cores are associated with a second memory channel.Type: GrantFiled: February 13, 2023Date of Patent: March 5, 2024Assignee: Intel CorporationInventors: 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
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Patent number: 11892950Abstract: Embodiments are generally directed to data prefetching for graphics data processing. An embodiment of an apparatus includes one or more processors including one or more graphics processing units (GPUs); and a plurality of caches to provide storage for the one or more GPUs, the plurality of caches including at least an L1 cache and an L3 cache, wherein the apparatus to provide intelligent prefetching of data by a prefetcher of a first GPU of the one or more GPUs including measuring a hit rate for the L1 cache; upon determining that the hit rate for the L1 cache is equal to or greater than a threshold value, limiting a prefetch of data to storage in the L3 cache, and upon determining that the hit rate for the L1 cache is less than a threshold value, allowing the prefetch of data to the L1 cache.Type: GrantFiled: July 15, 2022Date of Patent: February 6, 2024Assignee: INTEL CORPORATIONInventors: Vikranth Vemulapalli, Lakshminarayanan Striramassarma, Mike MacPherson, Aravindh Anantaraman, Ben Ashbaugh, Murali Ramadoss, William B. Sadler, Jonathan Pearce, Scott Janus, Brent Insko, Vasanth Ranganathan, Kamal Sinha, Arthur Hunter, Jr., Prasoonkumar Surti, Nicolas Galoppo von Borries, Joydeep Ray, Abhishek R. Appu, ElMoustapha Ould-Ahmed-Vall, Altug Koker, Sungye Kim, Subramaniam Maiyuran, Valentin Andrei
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Patent number: 11874715Abstract: Dynamic power budget allocation in a multi-processor system is described. In an example, an apparatus includes a plurality of processor units; and a power control component, the power control component to monitor power utilization of each of the plurality of processor units, wherein power consumed by the plurality of processor units is limited by a global power budget. The apparatus is to assign a workload to each of the processor units and is to establish an initial power budget for operation of each of the processor units, and, upon the apparatus determining that one or more processor units require an increased power budget based on one or more criteria, the apparatus is to dynamically reallocate an amount of the global power budget to the one or more processor units.Type: GrantFiled: October 14, 2022Date of Patent: January 16, 2024Assignee: INTEL CORPORATIONInventors: Nikos Kaburlasos, Iqbal Rajwani, Bhushan Borole, Kamal Sinha, Sanjeev Jahagirdar
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Publication number: 20240013337Abstract: A mechanism is described for detecting, at training time, information related to one or more tasks to be performed by the one or more processors according to a training dataset for a neural network, analyzing the information to determine one or more portions of hardware of a processor of the one or more processors that is configurable to support the one or more tasks, 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, and monitoring utilization of the hardware via a hardware unit of the graphics processor and, via a scheduler of the graphics processor, adjusting allocation of the one or more tasks to the one or more portions of the hardware based on the utilization.Type: ApplicationFiled: July 13, 2023Publication date: January 11, 2024Applicant: Intel CorporationInventors: 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
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Publication number: 20240004713Abstract: In an example, an apparatus comprises a plurality of execution units comprising at least a first type of execution unit and a second type of execution unit and logic, at least partially including hardware logic, to analyze a workload and assign the workload to one of the first type of execution unit or the second type of execution unit. Other embodiments are also disclosed and claimed.Type: ApplicationFiled: August 1, 2023Publication date: January 4, 2024Applicant: Intel CorporationInventors: Abhishek R. APPU, Altug KOKER, Balaji VEMBU, Joydeep RAY, Kamal SINHA, Prasoonkumar SURTI, Kiran C. VEERNAPU, Subramaniam MAIYURAN, Sanjeev S. Jahagirdar, Eric J. Asperheim, Guei-Yuan Lueh, David Puffer, Wenyin Fu, Nikos Kaburlasos, Bhushan M. Borole, Josh B. Mastronarde, Linda L. Hurd, Travis T. Schluessler, Tomasz Janczak, Abhishek Venkatesh, Kai Xiao, Slawomir Grajewski
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Publication number: 20240005136Abstract: 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: ApplicationFiled: July 12, 2023Publication date: January 4, 2024Applicant: Intel CorporationInventors: 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 Jahagirdar
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Publication number: 20230418355Abstract: Methods and apparatus relating to techniques for avoiding cache lookup for cold cache. In an example, an apparatus comprises logic, at least partially comprising hardware logic, to collect user information for a user of a data processing device, generate a user profile for the user of the data processing device from the user information, and set a power profile a processor in the data processing device using the user profile. Other embodiments are also disclosed and claimed.Type: ApplicationFiled: June 22, 2023Publication date: December 28, 2023Applicant: INTEL CORPORATIONInventors: Altug Koker, Abhishek R. Appu, Kiran C. Veernapu, Joydeep Ray, Balaji Vembu, Prasoonkumar Surti, Kamal Sinha, Eric J. Hoekstra, Wenyin Fu, Nikos Kaburlasos, Bhushan M. Borole, Travis T. Schluessler, Ankur N. Shah, Jonathan Kennedy
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Publication number: 20230394616Abstract: One embodiment provides a parallel processor comprising a hardware scheduler to schedule pipeline commands for compute operations to one or more of multiple types of compute units, a plurality of processing resources including a first sparse compute unit configured for input at a first level of sparsity and hybrid memory circuitry including a memory controller, a memory interface, and a second sparse compute unit configured for input at a second level of sparsity that is greater than the first level of sparsity.Type: ApplicationFiled: June 14, 2023Publication date: December 7, 2023Applicant: Intel CorporationInventors: Eriko Nurvitadhi, Balaji Vembu, Nicolas C. Galoppo Von Borries, Rajkishore Barik, Tsung-Han Lin, Kamal Sinha, Nadathur Rajagopalan Satish, Jeremy Bottleson, Farshad Akhbari, Altug Koker, Narayan Srinivasa, Dukhwan Kim, Sara S. Baghsorkhi, Justin E. Gottschlich, Feng Chen, Elmoustapha Ould-Ahmed-Vall, Kevin Nealis, Xiaoming Chen, Anbang Yao
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Patent number: 11829525Abstract: Systems, apparatuses and methods may provide away to enhance an augmented reality (AR) and/or virtual reality (VR) user experience with environmental information captured from sensors located in one or more physical environments. More particularly, systems, apparatuses and methods may provide a way to track, by an eye tracker sensor, a gaze of a user, and capture, by the sensors, environmental information. The systems, apparatuses and methods may render feedback, by one or more feedback devices or display device, for a portion of the environment information based on the gaze of the user.Type: GrantFiled: April 19, 2021Date of Patent: November 28, 2023Assignee: Intel CorporationInventors: Altug Koker, Michael Apodaca, Kai Xiao, Chandrasekaran Sakthivel, Jeffery S. Boles, Adam T. Lake, James M. Holland, Pattabhiraman K, Sayan Lahiri, Radhakrishnan Venkataraman, Kamal Sinha, Ankur N. Shah, Deepak S. Vembar, Abhishek R. Appu, Joydeep Ray, Elmoustapha Ould-Ahmed-Vall
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Patent number: 11816384Abstract: In accordance with some embodiments, the render rate is varied across and/or up and down the display screen. This may be done based on where the user is looking in order to reduce power consumption and/or increase performance. Specifically the screen display is separated into regions, such as quadrants. Each of these regions is rendered at a rate determined by at least one of what the user is currently looking at, what the user has looked at in the past and/or what it is predicted that the user will look at next. Areas of less focus may be rendered at a lower rate, reducing power consumption in some embodiments.Type: GrantFiled: October 4, 2022Date of Patent: November 14, 2023Assignee: Intel CorporationInventors: Eric J. Asperheim, Subramaniam Maiyuran, Kiran C. Veernapu, Sanjeev S. Jahagirdar, Balaji Vembu, Devan Burke, Philip R. Laws, Kamal Sinha, Abhishek R. Appu, Elmoustapha Ould-Ahmed-Vall, Peter L. Doyle, Joydeep Ray, Travis T. Schluessler, John H. Feit, Nikos Kaburlasos, Jacek Kwiatkowski, Altug Koker
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Publication number: 20230359461Abstract: One embodiment provides for a compute apparatus comprising a decode unit to decode a single instruction into a decoded instruction that specifies multiple operands including a multi-bit input value and a one-bit weight associated with a neural network, as well as an arithmetic logic unit including a multiplier, an adder, and an accumulator register. To execute the decoded instruction, the multiplier is to perform a fused operation including an exclusive not OR (XNOR) operation and a population count operation. The adder is configured to add the intermediate product to a value stored in the accumulator register and update the value stored in the accumulator register.Type: ApplicationFiled: May 11, 2023Publication date: November 9, 2023Applicant: Intel CorporationInventors: Kevin Nealis, Anbang Yao, Xiaoming Chen, Elmoustapha Ould-Ahmed-Vall, Sara S. Baghsorkhi, Eriko Nurvitadhi, Balaji Vembu, Nicolas C. Galoppo Von Borries, Rajkishore Barik, Tsung-Han Lin, Kamal Sinha
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Publication number: 20230351322Abstract: Systems and methods for evaluating attributes in supply chain management is disclosed. The system may receive data from a set of data sources corresponding to a supply chain associated with at least a product, pre-process the data based on integration of the data from each of the set of data sources, generate supply chain data based on the integrated data, analyze, via an orchestration engine, the supply chain data to assess an impact of the supply chain data on the supply chain, predict, via the orchestration engine, a state associated with a purchase event of the product in the supply chain, and generate a resolution flow to be executed in the supply chain for managing the predicted state associated with the purchase event of the product.Type: ApplicationFiled: March 30, 2023Publication date: November 2, 2023Applicant: ACCENTURE GLOBAL SOLUTIONS LIMITEDInventors: Swati SHARMA, Kishore P. DURG, Melissa TWINING-DAVIS, Antoni BARDAJÍ CUSÓ, Tamal DAS, Nirav Jagdish SAMPAT, Saran PRASAD, Surya N S CHAVALI, Arvind MAHESWARAN, Hitesh BHAGCHANDANI, Vinu VARGHESE, Rishi SAREEN, Shiv Kamal SINHA, Anuradha CHARI, Mateenuddin SHAIKH, Ajay DIVAKAR NAIK
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Patent number: 11803935Abstract: Techniques to improve performance of matrix multiply operations are described in which a compute kernel can specify one or more element-wise operations to perform on output of the compute kernel before the output is transferred to higher levels of a processor memory hierarchy.Type: GrantFiled: August 5, 2022Date of Patent: October 31, 2023Assignee: Intel CorporationInventors: Eriko Nurvitadhi, Balaji Vembu, Tsung-Han Lin, Kamal Sinha, Rajkishore Barik, Nicolas C. Galoppo Von Borries
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Patent number: 11797837Abstract: In an example, an apparatus comprises a plurality of execution units comprising at least a first type of execution unit and a second type of execution unit and logic, at least partially including hardware logic, to analyze a workload and assign the workload to one of the first type of execution unit or the second type of execution unit. Other embodiments are also disclosed and claimed.Type: GrantFiled: April 24, 2017Date of Patent: October 24, 2023Assignee: Intel CorporationInventors: Altug Koker, Abhishek R. Appu, Kamal Sinha, Joydeep Ray, Balaji Vembu, Elmoustapha Ould-Ahmed-Vall, Sara S. Baghsorkhi, Anbang Yao, Kevin Nealis, Xiaoming Chen, John C. Weast, Justin E. Gottschlich, Prasoonkumar Surti, Chandrasekaran Sakthivel, Farshad Akhbari, Nadathur Rajagopalan Satish, Liwei Ma, Jeremy Bottleson, Eriko Nurvitadhi, Travis T. Schluessler, Ankur N. Shah, Jonathan Kennedy, Vasanth Ranganathan, Sanjeev Jahagirdar
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Publication number: 20230334316Abstract: Described herein is a graphics processor comprising a memory device and a graphics processing cluster coupled with the memory device. The graphics processing cluster includes a plurality of graphics multiprocessors interconnected via a data interconnect. A graphics multiprocessor includes circuitry configured to load a modular neural network including a plurality of subnetworks, each of the plurality of subnetworks trained to perform a computer vision operation on a separate subject.Type: ApplicationFiled: May 9, 2023Publication date: October 19, 2023Applicant: Intel CorporationInventors: Altug Koker, Abhishek R. Appu, Kamal Sinha, Joydeep Ray, Balaji Vembu, Elmoustapha Ould-Ahmed-Vall, Sara S. Baghsorkhi, Anbang Yao, Kevin Nealis, Xiaoming Chen, John C. Weast, Justin E. Gottschlich, Prasoonkumar Surti, Chandrasekaran Sakthivel, Farshad Akhbari, Nadathur Rajagopalan Satish, Liwei Ma, Jeremy Bottleson, Eriko Nurvitadhi, Travis T. Schluessler, Ankur N. Shah, Jonathan Kennedy, Vasanth Ranganathan, Sanjeev Jahagirdar
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Patent number: 11762696Abstract: In an example, an apparatus comprises a plurality of execution units comprising at least a first type of execution unit and a second type of execution unit and logic, at least partially including hardware logic, to analyze a workload and assign the workload to one of the first type of execution unit or the second type of execution unit. Other embodiments are also disclosed and claimed.Type: GrantFiled: November 5, 2021Date of Patent: September 19, 2023Assignee: INTEL CORPORATIONInventors: Abhishek R Appu, Altug Koker, Balaji Vembu, Joydeep Ray, Kamal Sinha, Prasoonkumar Surti, Kiran C. Veernapu, Subramaniam Maiyuran, Sanjeev S. Jahagirdar, Eric J. Asperheim, Guei-Yuan Lueh, David Puffer, Wenyin Fu, Nikos Kaburlasos, Bhushan M. Borole, Josh B. Mastronarde, Linda L. Hurd, Travis T. Schluessler, Tomasz Janczak, Abhishek Venkatesh, Kai Xiao, Slawomir Grajewski
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Patent number: 11748106Abstract: A mechanism is described for facilitating fast data operations and for facilitating a finite state machine for machine learning at autonomous machines. A method of embodiments, as described herein, includes detecting input data to be used in computational tasks by a computation component of a processor including a graphics processor. The method may further include determining one or more frequently-used data values (FDVs) from the data, and pushing the one or more frequent data values to bypass the computational tasks.Type: GrantFiled: March 1, 2022Date of Patent: September 5, 2023Assignee: INTEL CORPORATIONInventors: Liwei Ma, Nadathur Rajagopalan Satish, Jeremy Bottleson, Farshad Akhbari, Eriko Nurvitadhi, Abhishek R. Appu, Altug Koker, Kamal Sinha, Joydeep Ray, Balaji Vembu, Vasanth Ranganathan, Sanjeev Jahagirdar
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Patent number: 11748606Abstract: 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: GrantFiled: May 11, 2021Date of Patent: September 5, 2023Assignee: INTEL CORPORATIONInventors: 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