Patents by Inventor Sanjeev Jahagirdar
Sanjeev Jahagirdar 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: 20240078630Abstract: Embodiments described herein are generally directed to improvements relating to power, latency, bandwidth and/or performance issues relating to GPU processing/caching. According to one embodiment, a system includes a producer intellectual property (IP) (e.g., a media IP), a compute core (e.g., a GPU or an AI-specific core of the GPU), a streaming buffer logically interposed between the producer IP and the compute core. The producer IP is operable to consume data from memory and output results to the streaming buffer. The compute core is operable to perform AI inference processing based on data consumed from the streaming buffer and output AI inference processing results to the memory.Type: ApplicationFiled: October 19, 2023Publication date: March 7, 2024Applicant: Intel CorporationInventors: Subramaniam Maiyuran, Durgaprasad Bilagi, Joydeep Ray, Scott Janus, Sanjeev Jahagirdar, Brent Insko, Lidong Xu, Abhishek R. Appu, James Holland, Vasanth Ranganathan, Nikos Kaburlasos, Altug Koker, Xinmin Tian, Guei-Yuan Lueh, Changliang Wang
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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: 20240013338Abstract: Embodiments described herein provide techniques to disaggregate an architecture of a system on a chip integrated circuit into multiple distinct chiplets that can be packaged onto a common chassis. In one embodiment, a graphics processing unit or parallel processor is composed from diverse silicon chiplets that are separately manufactured. A chiplet is an at least partially and distinctly packaged integrated circuit that includes distinct units of logic that can be assembled with other chiplets into a larger package. A diverse set of chiplets with different IP core logic can be assembled into a single device.Type: ApplicationFiled: September 20, 2023Publication date: January 11, 2024Applicant: Intel CorporationInventors: Naveen Matam, Lance Cheney, Eric Finley, Varghese George, Sanjeev Jahagirdar, Altug Koker, Josh Mastronarde, Iqbal Rajwani, Lakshminarayanan Striramassarma, Melaku Teshome, Vikranth Vemulapalli, Binoj Xavier
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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: 20240005443Abstract: Embodiments described herein provide techniques to disaggregate an architecture of a system on a chip integrated circuit into multiple distinct chiplets that can be packaged onto a common chassis. In one embodiment, a graphics processing unit or parallel processor is composed from diverse silicon chiplets that are separately manufactured. A chiplet is an at least partially and distinctly packaged integrated circuit that includes distinct units of logic that can be assembled with other chiplets into a larger package. A diverse set of chiplets with different IP core logic can be assembled into a single device.Type: ApplicationFiled: August 24, 2023Publication date: January 4, 2024Applicant: Intel CorporationInventors: Naveen Matam, Lance Cheney, Eric Finley, Varghese George, Sanjeev Jahagirdar, Altug Koker, Josh Mastronarde, Iqbal Rajwani, Lakshminarayanan Striramassarma, Melaku Teshome, Vikranth Vemulapalli, Binoj Xavier
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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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Patent number: 11861761Abstract: Embodiments described herein are generally directed to improvements relating to power, latency, bandwidth and/or performance issues relating to GPU processing/caching. According to one embodiment, a system includes a producer intellectual property (IP) (e.g., a media IP), a compute core (e.g., a GPU or an AI-specific core of the GPU), a streaming buffer logically interposed between the producer IP and the compute core. The producer IP is operable to consume data from memory and output results to the streaming buffer. The compute core is operable to perform AI inference processing based on data consumed from the streaming buffer and output AI inference processing results to the memory.Type: GrantFiled: November 11, 2020Date of Patent: January 2, 2024Assignee: Intel CorporationInventors: Subramaniam Maiyuran, Durgaprasad Bilagi, Joydeep Ray, Scott Janus, Sanjeev Jahagirdar, Brent Insko, Lidong Xu, Abhishek R. Appu, James Holland, Vasanth Ranganathan, Nikos Kaburlasos, Altug Koker, Xinmin Tian, Guei-Yuan Lueh, Changliang Wang
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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: 11763416Abstract: Embodiments described herein provide techniques to disaggregate an architecture of a system on a chip integrated circuit into multiple distinct chiplets that can be packaged onto a common chassis. In one embodiment, a graphics processing unit or parallel processor is composed from diverse silicon chiplets that are separately manufactured. A chiplet is an at least partially and distinctly packaged integrated circuit that includes distinct units of logic that can be assembled with other chiplets into a larger package. A diverse set of chiplets with different IP core logic can be assembled into a single device.Type: GrantFiled: October 13, 2021Date of Patent: September 19, 2023Assignee: Intel CorporationInventors: Naveen Matam, Lance Cheney, Eric Finley, Varghese George, Sanjeev Jahagirdar, Altug Koker, Josh Mastronarde, Iqbal Rajwani, Lakshminarayanan Striramassarma, Melaku Teshome, Vikranth Vemulapalli, Binoj Xavier
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Patent number: 11756150Abstract: Embodiments described herein provide techniques to disaggregate an architecture of a system on a chip integrated circuit into multiple distinct chiplets that can be packaged onto a common chassis. In one embodiment, a graphics processing unit or parallel processor is composed from diverse silicon chiplets that are separately manufactured. A chiplet is an at least partially and distinctly packaged integrated circuit that includes distinct units of logic that can be assembled with other chiplets into a larger package. A diverse set of chiplets with different IP core logic can be assembled into a single device.Type: GrantFiled: February 17, 2022Date of Patent: September 12, 2023Assignee: Intel CorporationInventors: Naveen Matam, Lance Cheney, Eric Finley, Varghese George, Sanjeev Jahagirdar, Altug Koker, Josh Mastronarde, Iqbal Rajwani, Lakshminarayanan Striramassarma, Melaku Teshome, Vikranth Vemulapalli, Binoj Xavier
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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: 11748841Abstract: A mechanism is described for facilitating inference coordination and processing utilization for machine learning. A method of embodiments, as described herein, includes limiting execution of workloads for the respective contexts of a plurality of contexts to a specified subset of a plurality of processing resources of a processing system according to physical resource slices of the processing system that are associated with the respective contexts of the plurality of contexts.Type: GrantFiled: July 22, 2022Date of Patent: September 5, 2023Assignee: 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: 20230260075Abstract: Embodiments described herein are generally directed to improvements relating to power, latency, bandwidth and/or performance issues relating to GPU processing/caching. According to one embodiment, a state of multiple intellectual property (IP) cores that have access to a common cache via a central fabric is observed. Responsive to the observed state being indicative of performance of a standalone workload by a first IP core of the multiple IP cores, the common cache is treated as a local cache of the first IP core by powering off the central fabric and causing the first IP core to access the common cache via a low power access path between the first IP core and the common cache that is outside of the central fabric.Type: ApplicationFiled: April 24, 2023Publication date: August 17, 2023Applicant: Intel CorporationInventors: Subramaniam Maiyuran, Durgaprasad Bilagi, Joydeep Ray, Scott Janus, Sanjeev Jahagirdar, Brent Insko, Lidong Xu, Abhishek R. Appu, James Holland, Vasanth Ranganathan, Nikos Kaburlasos, Altug Koker, Xinmin Tian, Guei-Yuan Lueh, Changliang Wang
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Patent number: 11720355Abstract: One embodiment provides a graphics processor comprising a memory controller and a graphics processing resource coupled with the memory controller. The graphics processing resource includes circuitry configured to execute an instruction to perform a matrix operation on first input including weight data and second input including input activation data, generate intermediate data based on a result of the matrix operation, quantize the intermediate data to a floating-point format determined based on a statistical distribution of first output data, and output, as second output data, quantized intermediate data in a determined floating-point format.Type: GrantFiled: June 7, 2022Date of Patent: August 8, 2023Assignee: Intel CorporationInventors: Himanshu Kaul, Mark A. Anders, Sanu K. Mathew, Anbang Yao, Joydeep Ray, Ping T. Tang, Michael S. Strickland, Xiaoming Chen, Tatiana Shpeisman, Abhishek R. Appu, Altug Koker, Kamal Sinha, Balaji Vembu, Nicolas C. Galoppo Von Borries, Eriko Nurvitadhi, Rajkishore Barik, Tsung-Han Lin, Vasanth Ranganathan, Sanjeev Jahagirdar
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Patent number: 11592817Abstract: A mechanism is described for facilitating storage management for machine learning at autonomous machines. A method of embodiments, as described herein, includes detecting one or more components associated with machine learning, where the one or more components include memory and a processor coupled to the memory, and where the processor includes a graphics processor. The method may further include allocating a storage portion of the memory and a hardware portion of the processor to a machine learning training set, where the storage and hardware portions are precise for implementation and processing of the training set.Type: GrantFiled: April 28, 2017Date of Patent: February 28, 2023Assignee: INTEL CORPORATIONInventors: Abhishek R. Appu, John C. Weast, Sara S. Baghsorkhi, Justin E. Gottschlich, Prasoonkumar Surti, Chandrasekaran Sakthivel, Altug Koker, Farshad Akhbari, Feng Chen, Dukhwan Kim, Narayan Srinivasa, Nadathur Rajagopalan Satish, Kamal Sinha, Joydeep Ray, Balaji Vembu, Mike B. Macpherson, Linda L. Hurd, Sanjeev Jahagirdar, Vasanth Ranganathan
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Publication number: 20230046506Abstract: One embodiment provides for a graphics processing unit to accelerate machine-learning operations, the graphics processing unit comprising a multiprocessor having a single instruction, multiple thread (SIMT) architecture, the multiprocessor to execute at least one single instruction; and a first compute unit included within the multiprocessor, the at least one single instruction to cause the first compute unit to perform a two-dimensional matrix multiply and accumulate operation, wherein to perform the two-dimensional matrix multiply and accumulate operation includes to compute an intermediate product of 16-bit operands and to compute a 32-bit sum based on the intermediate product.Type: ApplicationFiled: October 17, 2022Publication date: February 16, 2023Applicant: Intel CorporationInventors: Himanshu Kaul, Mark A. Anders, Sanu K. Mathew, Anbang Yao, Joydeep Ray, Ping T. Tang, Michael S. Strickland, Xiaoming Chen, Tatiana Shpeisman, Abhishek R. Appu, Altug Koker, Kamal Sinha, Balaji Vembu, Nicolas C. Galoppo Von Borries, Eriko Nurvitadhi, Rajkishore Barik, Tsung-Han Lin, Vasanth Ranganathan, Sanjeev Jahagirdar
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Publication number: 20230030396Abstract: 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: ApplicationFiled: October 14, 2022Publication date: February 2, 2023Applicant: Intel CorporationInventors: Nikos Kaburlasos, Iqbal Rajwani, Bhushan Borole, Kamal Sinha, Sanjeev Jahagirdar
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Publication number: 20220366527Abstract: A mechanism is described for facilitating inference coordination and processing utilization for machine learning. A method of embodiments, as described herein, includes limiting execution of workloads for the respective contexts of a plurality of contexts to a specified subset of a plurality of processing resources of a processing system according to physical resource slices of the processing system that are associated with the respective contexts of the plurality of contexts.Type: ApplicationFiled: July 22, 2022Publication date: November 17, 2022Applicant: 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: 20220357945Abstract: One embodiment provides a graphics processor comprising a memory controller and a graphics processing resource coupled with the memory controller. The graphics processing resource includes circuitry configured to execute an instruction to perform a matrix operation on first input including weight data and second input including input activation data, generate intermediate data based on a result of the matrix operation, quantize the intermediate data to a floating-point format determined based on a statistical distribution of first output data, and output, as second output data, quantized intermediate data in a determined floating-point format.Type: ApplicationFiled: June 7, 2022Publication date: November 10, 2022Applicant: Intel CorporationInventors: Himanshu Kaul, Mark A. Anders, Sanu K. Mathew, Anbang Yao, Joydeep Ray, Ping T. Tang, Michael S. Strickland, Xiaoming Chen, Tatiana Shpeisman, Abhishek R. Appu, Altug Koker, Kamal Sinha, Balaji Vembu, Nicolas C. Galoppo Von Borries, Eriko Nurvitadhi, Rajkishore Barik, Tsung-Han Lin, Vasanth Ranganathan, Sanjeev Jahagirdar