公开(公告)号：US10346166B2

公开(公告)日：2019-07-09

申请号：US15581080

申请日：2017-04-28

Applicant: Intel Corporation

Inventor： Feng Chen ,  Narayan Srinivasa ,  Abhishek R. Appu ,  Altug Koker ,  Kamal Sinha ,  Balaji Vembu ,  Joydeep Ray ,  Nicolas C. Galoppo Von Borries ,  Prasoonkumar Surti ,  Ben J. Ashbaugh ,  Sanjeev Jahagirdar ,  Vasanth Ranganathan

IPC: G06T1/00 ,  G06F9/30 ,  G06F9/38 ,  G06F12/0862 ,  G06F12/0875 ,  G06F9/50

Abstract: A mechanism is described for facilitating intelligent dispatching and vectorizing at autonomous machines. A method of embodiments, as described herein, includes detecting a plurality of threads corresponding to a plurality of workloads associated with tasks relating to a graphics processor. The method may further include determining a first set of threads of the plurality of threads that are similar to each other or have adjacent surfaces, and physically clustering the first set of threads close together using a first set of adjacent compute blocks.

公开(公告)号：US20180314521A1

公开(公告)日：2018-11-01

申请号：US15581080

申请日：2017-04-28

Applicant: Intel Corporation

Inventor： Feng Chen ,  Narayan Srinivasa ,  Abhishek R. Appu ,  Altug Koker ,  Kamal Sinha ,  Balaji Vembu ,  Joydeep Ray ,  Nicolas C. Galoppo Von Borries ,  Prasoonkumar Surti ,  Ben J. Ashbaugh ,  Sanjeev Jahagirdar ,  Vasanth Ranganathan

IPC: G06F9/30 ,  G06F9/38 ,  G06F12/0862 ,  G06F12/0875

CPC classification number: G06F9/3009 ,  G06F9/30036 ,  G06F9/30145 ,  G06F9/3836 ,  G06F9/3867 ,  G06F9/3887 ,  G06F9/5033 ,  G06F9/5066 ,  G06F12/0862 ,  G06F12/0875 ,  G06F2212/452 ,  G06F2212/602

Abstract: A mechanism is described for facilitating intelligent dispatching and vectorizing at autonomous machines. A method of embodiments, as described herein, includes detecting a plurality of threads corresponding to a plurality of workloads associated with tasks relating to a graphics processor. The method may further include determining a first set of threads of the plurality of threads that are similar to each other or have adjacent surfaces, and physically clustering the first set of threads close together using a first set of adjacent compute blocks.

公开(公告)号：US20180174023A1

公开(公告)日：2018-06-21

申请号：US15385299

申请日：2016-12-20

Applicant: Intel Corporation

Inventor： Nabil Imam ,  Narayan Srinivasa

IPC: G06N3/04 ,  G06N3/08 ,  G06N3/063

CPC classification number: G06N3/049 ,  G06N3/063 ,  G06N3/0635 ,  G06N3/08 ,  G06N3/088

Abstract: A spike timing dependent plasticity (STDP) rule is applied in a spiking neural network (SNN) that includes artificial synapses bi-directionally connecting artificial neurons in the SNN to model locations within a physical environment. A first neuron is activated to cause a spike wave to propagate from the first neuron to other neurons in the SNN. Propagation of the spike wave causes synaptic weights of a subset of the synapses to be increased based on the STDP rule. A second neuron is activated after propagation of the spike wave to cause a spike chain to propagate along a path from the second neuron to the first neuron, based on the changes to the synaptic weights. A physical path is determined from the second to the first neuron based on the spike chain, and a signal may be sent to a controller of an autonomous device to cause the autonomous to navigate the physical path.

公开(公告)号：US12198221B2

公开(公告)日：2025-01-14

申请号：US18436494

申请日：2024-02-08

Applicant: Intel Corporation

Inventor： 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

IPC: G06T1/20 ,  G06F8/41 ,  G06F9/455 ,  G06F9/50 ,  G06N3/044 ,  G06N3/045 ,  G06N3/063 ,  G06N3/084

Abstract: 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.

公开(公告)号：US20250005703A1

公开(公告)日：2025-01-02

申请号：US18773094

申请日：2024-07-15

Applicant: Intel Corporation

Inventor： 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

IPC: G06T1/20 ,  G06F3/14 ,  G06F9/30 ,  G06F9/38 ,  G06N3/044 ,  G06N3/045 ,  G06N3/063 ,  G06N3/084 ,  G06T15/00 ,  G06T15/04 ,  G09G5/36

Abstract: 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.

公开(公告)号：US20240112460A1

公开(公告)日：2024-04-04

申请号：US18478335

申请日：2023-09-29

Applicant: Intel Corporation

Inventor： Narayan Srinivasa

IPC: G06V20/00 ,  G06F18/214 ,  G06F30/34 ,  G06N3/08 ,  G06V10/44 ,  G06V10/764 ,  G06V10/774 ,  G09G3/20 ,  G09G3/36

CPC classification number: G06V20/00 ,  G06F18/214 ,  G06F30/34 ,  G06N3/08 ,  G06V10/454 ,  G06V10/764 ,  G06V10/774 ,  G09G3/2003 ,  G09G3/3607

Abstract: Apparatuses, methods, and articles of manufacture are disclosed. An example apparatus includes processor circuitry to assign a location value hyperdimensional vector (HDV) to a location in an image of a first patch of one or more pixels, assign at least a first channel HDV to the first patch, determine at least one pixel intensity value HDV for each of the one or more pixels in the first patch, bind together each of the pixel intensity value HDVs into at least one patch intensity value HDV, bind together the at least first channel HDV and the at least one patch intensity value HDV to produce a patch consensus intensity HDV, and generate a first hyperdimensional representation patch value HDV of the first patch by binding together at least a combination of the patch consensus intensity HDV and the location value HDV.

公开(公告)号：US20240005136A1

公开(公告)日：2024-01-04

申请号：US18351124

申请日：2023-07-12

Applicant: Intel Corporation

Inventor： 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

IPC: G06N3/063 ,  G06N3/08 ,  G06N3/04 ,  G06T1/20 ,  G06F9/30 ,  G06T15/00 ,  G06F15/78 ,  G06F15/76 ,  G06F1/3287 ,  G06F1/3293 ,  G06N3/084 ,  G06N3/044 ,  G06N3/045

CPC classification number: G06N3/063 ,  G06N3/08 ,  G06N3/04 ,  G06T1/20 ,  G06F9/30014 ,  G06T15/005 ,  G06F15/78 ,  G06F15/76 ,  G06F9/30036 ,  G06F1/3287 ,  G06F1/3293 ,  G06N3/084 ,  G06N3/044 ,  G06N3/045 ,  G06T1/60

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.

公开(公告)号：US11651199B2

公开(公告)日：2023-05-16

申请号：US16644446

申请日：2017-12-19

Applicant: Intel Corporation

Inventor： Narayan Srinivasa

IPC: G06N3/04 ,  G06K9/62 ,  G06N3/08 ,  G06V20/40 ,  G06N3/049

CPC classification number: G06N3/049 ,  G06K9/627 ,  G06K9/6257 ,  G06N3/0481 ,  G06N3/08 ,  G06V20/42

Abstract: Techniques and mechanisms for processing differential video data with a spiking neural network to provide action recognition functionality. In an embodiment, the spiking neural network is coupled to receive and process a first one or more spike trains which represent an encoded version of a sequence comprising frames of differential video data. In turn, the frames of differential video data are each based on a difference between a respective two frames of raw video data. Based on the processing of the first one or more spike trains, the spiking neural network may output a second one or more spike trains. In another embodiment, the second one or more spike trains are provided to train the spiked neural network to recognize an activity type, or to classify a video sequence as including a representation of an instance of the activity type.

公开(公告)号：US20220200655A1

公开(公告)日：2022-06-23

申请号：US17132893

申请日：2020-12-23

Applicant: Intel Corporation

Inventor： Mohamed A. Abdelmoneum ,  Nasser Kurd ,  Thripthi Hegde ,  Narayan Srinivasa ,  Peter Sagazio

IPC: H04B1/7156 ,  H04B1/7136 ,  H04B1/7143 ,  H04L9/08

Abstract: A clock buffer or driver is gated pending reception of verifiable crypto keys. These clock buffer or divers remain gated, thus disabling a processor from any meaningful function, till crypto keys are decoded, verified, and applied to the clock buffer or driver. A low frequency pseudorandom frequency hopping time sequence is generated and used for randomizing spread-spectrum to modulate a reference clock (or output clock) of a frequency synthesizer. This hopping time sequence holds the key to unlocking the crypto keys. The PWM modulated crypto keys are carried by the hopping time sequence. To decode the PWM modulated crypto keys, the hopping time sequence is used. The reference clock which is modulated with crypto keys in the spread-spectrum is sent to a decoder (in a processor) along with the hopping time sequence. The crypto keys are decoded and then used to un-gate the clock buffer.

公开(公告)号：US11348198B2

公开(公告)日：2022-05-31

申请号：US17145885

申请日：2021-01-11

Applicant: Intel Corporation

Inventor： 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

IPC: G06T1/20 ,  G06F9/455 ,  G06F9/50 ,  G06N3/04 ,  G06N3/063 ,  G06N3/08 ,  G06F8/41

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.