Patents by Inventor Tatiana Shpeisman
Tatiana Shpeisman 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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Patent number: 11934934Abstract: An apparatus to facilitate optimization of a convolutional neural network (CNN) is disclosed. The apparatus includes optimization logic to receive a CNN model having a list of instructions and including pruning logic to optimize the list of instructions by eliminating branches in the list of instructions that comprise a weight value of 0.Type: GrantFiled: April 17, 2017Date of Patent: March 19, 2024Assignee: Intel CorporationInventors: Liwei Ma, Elmoustapha Ould- Ahmed-Vall, Barath Lakshmanan, Ben J. Ashbaugh, Jingyi Jin, Jeremy Bottleson, Mike B. Macpherson, Kevin Nealis, Dhawal Srivastava, Joydeep Ray, Ping T. Tang, Michael S. Strickland, Xiaoming Chen, Anbang Yao, Tatiana Shpeisman, Altug Koker, Abhishek R. Appu
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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: 20240004829Abstract: An integrated circuit (IC) package apparatus is disclosed. The IC package includes one or more processing units and a bridge, mounted below the one or more processing unit, including one or more arithmetic logic units (ALUs) to perform atomic operations.Type: ApplicationFiled: July 12, 2023Publication date: January 4, 2024Applicant: Intel CorporationInventors: Altug Koker, Farshad Akhbari, Feng Chen, Dukhwan Kim, Narayan Srinivasa, Nadathur Rajagopalan Satish, Liwei Ma, Jeremy Bottleson, Eriko Nurvitadhi, Joydeep Ray, Ping T. Tang, Michael S. Strickland, Xiaoming Chen, Tatiana Shpeisman, Abhishek R. Appu
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Publication number: 20230315481Abstract: Described herein is a general-purpose graphics processing unit including a multiprocessor having a single instruction, multiple thread, SIMT, architecture. The multiprocessor comprises multiple sets of compute units each having a first logic unit configured to perform floating-point operations and a second logic unit configured to perform integer operations, with a thread of the floating-point instruction being executed in parallel with a thread of the integer instruction.Type: ApplicationFiled: May 4, 2023Publication date: October 5, 2023Applicant: Intel CorporationInventors: ELMOUSTAPHA OULD-AHMED-VALL, BARATH LAKSHMANAN, TATIANA SHPEISMAN, Joydeep Ray, Ping T. Tang, Michael Strickland, Xiaoming Chen, Anbang Yao, Ben J. Ashbaugh, Linda L. Hurd, Liwei Ma
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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
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Patent number: 11748298Abstract: An integrated circuit (IC) package apparatus is disclosed. The IC package includes one or more processing units and a bridge, mounted below the one or more processing unit, including one or more arithmetic logic units (ALUs) to perform atomic operations.Type: GrantFiled: May 27, 2022Date of Patent: September 5, 2023Assignee: INTEL CORPORATIONInventors: Altug Koker, Farshad Akhbari, Feng Chen, Dukhwan Kim, Narayan Srinivasa, Nadathur Rajagopalan Satish, Liwei Ma, Jeremy Bottleson, Eriko Nurvitadhi, Joydeep Ray, Ping T. Tang, Michael S. Strickland, Xiaoming Chen, Tatiana Shpeisman, Abhishek R. Appu
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Patent number: 11727246Abstract: Embodiments provide systems and methods which facilitate optimization of a convolutional neural network (CNN). One embodiment provides for a non-transitory machine-readable medium storing instructions that cause one or more processors to perform operations comprising processing a trained convolutional neural network (CNN) to generate a processed CNN, the trained CNN having weights in a floating-point format. Processing the trained CNN includes quantizing the weights in the floating-point format to generate weights in an integer format. Quantizing the weights includes generating a quantization table to enable non-uniform quantization of the weights and quantizing the weights from the floating-point format to the integer format using the quantization table. The operations additionally comprise performing an inference operation utilizing the processed CNN with the integer format weights.Type: GrantFiled: February 22, 2019Date of Patent: August 15, 2023Assignee: Intel CorporationInventors: Liwei Ma, Elmoustapha Ould-Ahmed-Vall, Barath Lakshmanan, Ben J. Ashbaugh, Jingyi Jin, Jeremy Bottleson, Mike B. Macpherson, Kevin Nealis, Dhawal Srivastava, Joydeep Ray, Ping T. Tang, Michael S. Strickland, Xiaoming Chen, Anbang Yao, Tatiana Shpeisman, Altug Koker, Abhishek R. Appu
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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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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: 20230039729Abstract: Methods and apparatus relating to autonomous vehicle neural network optimization techniques are described. In an embodiment, the difference between a first training dataset to be used for a neural network and a second training dataset to be used for the neural network is detected. The second training dataset is authenticated in response to the detection of the difference. The neural network is used to assist in an autonomous vehicle/driving. Other embodiments are also disclosed and claimed.Type: ApplicationFiled: October 11, 2022Publication date: February 9, 2023Applicant: Intel CorporationInventors: Abhishek R. Appu, Altug Koker, Linda L. Hurd, Dukhwan Kim, Mike B. MacPherson, John C. Weast, Justin E. Gottschlich, Jingyi Jin, Barath Lakshmanan, Chandrasekaran Sakthivel, Michael S. Strickland, Joydeep Ray, Kamal Sinha, Prasoonkumar Surti, Balaji Vembu, Ping T. Tang, Anbang Yao, Tatiana Shpeisman, Xiaoming Chen
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Publication number: 20230027203Abstract: An integrated circuit (IC) package apparatus is disclosed. The IC package includes one or more processing units and a bridge, mounted below the one or more processing unit, including one or more arithmetic logic units (ALUs) to perform atomic operations.Type: ApplicationFiled: May 27, 2022Publication date: January 26, 2023Applicant: Intel CorporationInventors: Altug Koker, Farshad Akhbari, Feng Chen, Dukhwan Kim, Narayan Srinivasa, Nadathur Rajagopalan Satish, Liwei Ma, Jeremy Bottleson, Eriko Nurvitadhi, Joydeep Ray, Ping T. Tang, Michael S. Strickland, Xiaoming Chen, Tatiana Shpeisman, Abhishek R. Appu
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Publication number: 20220391679Abstract: One embodiment provides a graphics processor comprising an instruction cache to store an instruction and a compute block configured to perform multiply-accumulate operations in response to execution of the instruction. The compute block includes a scheduler to schedule a plurality of threads for execution of the instruction and multiply-accumulate circuitry configured to execute the instruction via the plurality of threads, wherein the multiply-accumulate circuitry includes a plurality of functional units configured to process, in parallel via the plurality of threads, a corresponding plurality of matrix elements to multiply a first matrix and a second matrix, and to multiply the first matrix and the second matrix includes to multiply data elements in a row of the first matrix by corresponding data elements in a column of the second matrix to generate a plurality of products.Type: ApplicationFiled: August 11, 2022Publication date: December 8, 2022Applicant: Intel CorporationInventors: Rajkishore Barik, Elmoustapha Ould-Ahmed-Vall, Xiaoming Chen, Dhawal Srivastava, Anbang Yao, Kevin Nealis, Eriko Nurvitadhi, Sara S. Baghsorkhi, Balaji Vembu, Tatiana Shpeisman, Ping T. Tang
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Publication number: 20220382555Abstract: One embodiment provides for a graphics processing unit (GPU) to accelerate machine learning operations, the GPU comprising an instruction cache to store a first instruction and a second instruction, the first instruction to cause the GPU to perform a floating-point operation, including a multi-dimensional floating-point operation, and the second instruction to cause the GPU to perform an integer operation; and a general-purpose graphics compute unit having a single instruction, multiple thread architecture, the general-purpose graphics compute unit to concurrently execute the first instruction and the second instruction.Type: ApplicationFiled: June 14, 2022Publication date: December 1, 2022Applicant: Intel CorporationInventors: ELMOUSTAPHA OULD-AHMED-VALL, BARATH LAKSHMANAN, TATIANA SHPEISMAN, Joydeep Ray, Ping T. Tang, Michael Strickland, Xiaoming Chen, Anbang Yao, Ben J. Ashbaugh, Linda L. Hurd, Liwei Ma
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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
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Patent number: 11475286Abstract: One embodiment provides an apparatus comprising an instruction cache to store a plurality of instructions, a scheduler unit coupled to the instruction cache, the scheduler unit to schedule the plurality of instructions for execution, an instruction fetch and decode unit to decode the plurality of instructions to determine a set of operations to perform in response, one or more compute blocks to perform parallel multiply-accumulate operations based on the instruction fetch and decode unit decoding a first instruction of the plurality of instructions, and matrix multiplication logic to perform matrix multiplication operations based on the instruction fetch and decode unit decoding a second instruction of the plurality of instructions.Type: GrantFiled: December 21, 2021Date of Patent: October 18, 2022Assignee: Intel CorporationInventors: Rajkishore Barik, Elmoustapha Ould-Ahmed-Vall, Xiaoming Chen, Dhawal Srivastava, Anbang Yao, Kevin Nealis, Eriko Nurvitadhi, Sara S. Baghsorkhi, Balaji Vembu, Tatiana Shpeisman, Ping T. Tang
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Patent number: 11461107Abstract: One embodiment provides for a general-purpose graphics processing unit comprising a streaming multiprocessor having a single instruction, multiple thread (SIMT) architecture including hardware multithreading. The streaming multiprocessor comprises multiple processing blocks including multiple processing cores. The processing cores include independent integer and floating-point data paths that are configurable to concurrently execute multiple independent instructions. A memory is coupled with the multiple processing blocks.Type: GrantFiled: December 20, 2018Date of Patent: October 4, 2022Assignee: Intel CorporationInventors: Elmoustapha Ould-Ahmed-Vall, Barath Lakshmanan, Tatiana Shpeisman, Joydeep Ray, Ping T. Tang, Michael Strickland, Xiaoming Chen, Anbang Yao, Ben J. Ashbaugh, Linda L. Hurd, Liwei Ma
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Publication number: 20220261948Abstract: An apparatus to facilitate compute optimization is disclosed. The apparatus includes a mixed precision core including mixed-precision execution circuitry to execute one or more of the mixed-precision instructions to perform a mixed-precision dot-product operation comprising to perform a set of multiply and accumulate operations.Type: ApplicationFiled: March 1, 2022Publication date: August 18, 2022Applicant: Intel CorporationInventors: 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
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Patent number: 11409537Abstract: One embodiment provides for a graphics processing unit (GPU) to accelerate machine learning operations, the GPU comprising an instruction cache to store a first instruction and a second instruction, the first instruction to cause the GPU to perform a floating-point operation, including a multi-dimensional floating-point operation, and the second instruction to cause the GPU to perform an integer operation; and a general-purpose graphics compute unit having a single instruction, multiple thread (SIMT) architecture, the general-purpose graphics compute unit to simultaneously execute the first instruction and the second instruction, wherein the integer operation corresponds to a memory address calculation.Type: GrantFiled: November 21, 2017Date of Patent: August 9, 2022Assignee: Intel CorporationInventors: Elmoustapha Ould-Ahmed-Vall, Barath Lakshmanan, Tatiana Shpeisman, Joydeep Ray, Ping T. Tang, Michael Strickland, Xiaoming Chen, Anbang Yao, Ben J. Ashbaugh, Linda L. Hurd, Liwei Ma
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Patent number: 11393211Abstract: A mechanism is described for facilitating person tracking and data security in machine learning at autonomous machines. A method of embodiments, as described herein, includes detecting, by a camera associated with one or more trackers, a person within a physical vicinity, where detecting includes capturing one or more images the person. The method may further include tracking, by the one or more trackers, the person based on the one or more images of the person, where tracking includes collect tracking data relating to the person. The method may further include selecting a tracker of the one or more trackers as a preferred tracker based on the tracking data.Type: GrantFiled: February 11, 2021Date of Patent: July 19, 2022Assignee: Intel CorporationInventors: Mayuresh M. Varerkar, Barnan Das, Narayan Biswal, Stanley J. Baran, Gokcen Cilingir, Nilesh V. Shah, Archie Sharma, Sherine Abdelhak, Sachin Godse, Farshad Akhbari, Narayan Srinivasa, Altug Koker, Nadathur Rajagopalan Satish, Dukhwan Kim, Feng Chen, Abhishek R. Appu, Joydeep Ray, Ping T. Tang, Michael S. Strickland, Xiaoming Chen, Anbang Yao, Tatiana Shpeisman, Vasanth Ranganathan, Sanjeev Jahagirdar
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Patent number: 11360767Abstract: A processing apparatus is provided comprising a multiprocessor having a multithreaded architecture. The multiprocessor can execute at least one single instruction to perform parallel mixed precision matrix operations. In one embodiment the apparatus includes a memory interface and an array of multiprocessors coupled to the memory interface. At least one multiprocessor in the array of multiprocessors is configured to execute a fused multiply-add instruction in parallel across multiple threads.Type: GrantFiled: July 6, 2021Date of Patent: June 14, 2022Assignee: 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