公开(公告)号：US11836240B2

公开(公告)日：2023-12-05

申请号：US18157154

申请日：2023-01-20

申请人： Intel Corporation

发明人： Yen-Kuang Chen ,  Shao-Wen Yang ,  Ibrahima J. Ndiour ,  Yiting Liao ,  Vallabhajosyula S. Somayazulu ,  Omesh Tickoo ,  Srenivas Varadarajan

IPC分类号： G06F21/44 ,  H04L9/06 ,  G06F21/64 ,  G06F21/53 ,  G06N5/022 ,  G06F21/45 ,  H04L9/32 ,  H04W4/70 ,  G06F16/538 ,  G06F16/535 ,  G06F16/54 ,  G06F21/62 ,  G06F9/50 ,  G06N3/04 ,  G06N3/063 ,  G06V10/44 ,  G06V20/00 ,  G06V40/20 ,  G06V40/16 ,  G06F9/48 ,  H04L67/51 ,  G06T7/11 ,  G06V10/96 ,  G06V30/262 ,  G06K15/02 ,  G06F18/24 ,  G06F18/21 ,  G06F18/22 ,  G06F18/211 ,  G06F18/213 ,  G06F18/2413 ,  G06N3/045 ,  G06V30/19 ,  G06V10/82 ,  G06V10/94 ,  G06V10/75 ,  G06V10/20 ,  G06V10/40 ,  G06N3/08 ,  H04L67/12 ,  H04N19/80 ,  G06F16/951 ,  H04N19/46 ,  G06T7/70 ,  H04W12/02 ,  H04L9/00 ,  H04N19/12 ,  H04N19/124 ,  H04N19/167 ,  H04N19/172 ,  H04N19/176 ,  H04N19/44 ,  H04N19/48 ,  H04N19/513 ,  G06T7/20 ,  G06F18/243 ,  G06V30/194 ,  H04N19/42 ,  H04N19/625 ,  H04N19/63 ,  G06T7/223 ,  H04L67/10

CPC分类号： G06F21/44 ,  G06F9/4881 ,  G06F9/5044 ,  G06F9/5066 ,  G06F9/5072 ,  G06F16/535 ,  G06F16/538 ,  G06F16/54 ,  G06F16/951 ,  G06F18/21 ,  G06F18/211 ,  G06F18/213 ,  G06F18/2163 ,  G06F18/22 ,  G06F18/24 ,  G06F18/24143 ,  G06F21/45 ,  G06F21/53 ,  G06F21/6254 ,  G06F21/64 ,  G06K15/1886 ,  G06N3/04 ,  G06N3/045 ,  G06N3/063 ,  G06N3/08 ,  G06N5/022 ,  G06T7/11 ,  G06T7/70 ,  G06V10/20 ,  G06V10/40 ,  G06V10/454 ,  G06V10/75 ,  G06V10/82 ,  G06V10/95 ,  G06V10/96 ,  G06V20/00 ,  G06V30/19173 ,  G06V30/274 ,  G06V40/161 ,  G06V40/20 ,  H04L9/0643 ,  H04L9/3239 ,  H04L67/12 ,  H04L67/51 ,  H04N19/46 ,  H04N19/80 ,  H04W4/70 ,  G06F18/24323 ,  G06F2209/503 ,  G06F2209/506 ,  G06F2221/2117 ,  G06T7/20 ,  G06T7/223 ,  G06T2207/10016 ,  G06T2207/20021 ,  G06T2207/20024 ,  G06T2207/20052 ,  G06T2207/20056 ,  G06T2207/20064 ,  G06T2207/20084 ,  G06T2207/20221 ,  G06T2207/30242 ,  G06V30/194 ,  G06V2201/10 ,  H04L9/50 ,  H04L67/10 ,  H04N19/12 ,  H04N19/124 ,  H04N19/167 ,  H04N19/172 ,  H04N19/176 ,  H04N19/42 ,  H04N19/44 ,  H04N19/48 ,  H04N19/513 ,  H04N19/625 ,  H04N19/63 ,  H04W12/02

摘要： In one embodiment, an apparatus comprises a memory and a processor. The memory is to store visual data associated with a visual representation captured by one or more sensors. The processor is to: obtain the visual data associated with the visual representation captured by the one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data; process the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, wherein the plurality of layers comprises a plurality of filters, and wherein the plurality of filters comprises one or more pixel-domain filters to perform processing associated with uncompressed data and one or more compressed-domain filters to perform processing associated with compressed data; and classify the visual data based on an output of the CNN.

公开(公告)号：US11531850B2

公开(公告)日：2022-12-20

申请号：US16947590

申请日：2020-08-07

申请人： Intel Corporation

发明人： Yen-Kuang Chen ,  Shao-Wen Yang ,  Ibrahima J. Ndiour ,  Yiting Liao ,  Vallabhajosyula S. Somayazulu ,  Omesh Tickoo ,  Srenivas Varadarajan

IPC分类号： G06K9/62 ,  G06F9/48 ,  G06F9/50 ,  G06F16/535 ,  G06F16/538 ,  G06F16/951 ,  G06F21/44 ,  G06F21/45 ,  G06F21/53 ,  G06F21/62 ,  G06F21/64 ,  H04L9/06 ,  G06N5/02 ,  G06N3/04 ,  H04L9/32 ,  H04W4/70 ,  G06F16/54 ,  G06N3/063 ,  G06V10/20 ,  G06V10/40 ,  G06V10/75 ,  G06V10/44 ,  G06V20/00 ,  G06V40/20 ,  G06V40/16 ,  H04L67/51 ,  G06T7/11 ,  G06V10/96 ,  G06V30/262 ,  G06K15/02 ,  G06N3/08 ,  H04L67/12 ,  H04N19/80 ,  H04N19/46 ,  G06T7/70 ,  H04W12/02 ,  H04L9/00 ,  H04N19/12 ,  H04N19/124 ,  H04N19/167 ,  H04N19/172 ,  H04N19/176 ,  H04N19/44 ,  H04N19/48 ,  H04N19/513 ,  G06V30/194 ,  G06T7/20 ,  H04N19/42 ,  H04N19/625 ,  H04N19/63 ,  G06T7/223 ,  H04L67/10

摘要： In one embodiment, an apparatus comprises a storage device and a processor. The storage device may store a plurality of compressed images comprising one or more compressed master images and one or more compressed slave images. The processor may: identify an uncompressed image; access context information associated with the uncompressed image and the one or more compressed master images; determine, based on the context information, whether the uncompressed image is associated with a corresponding master image; upon a determination that the uncompressed image is associated with the corresponding master image, compress the uncompressed image into a corresponding compressed image with reference to the corresponding master image; upon a determination that the uncompressed image is not associated with the corresponding master image, compress the uncompressed image into the corresponding compressed image without reference to the one or more compressed master images; and store the corresponding compressed image on the storage device.

公开(公告)号：US20220191537A1

公开(公告)日：2022-06-16

申请号：US17509246

申请日：2021-10-25

申请人： Intel Corporation

发明人： Yiting Liao ,  Yen-Kuang Chen ,  Shao-Wen Yang ,  Vallabhajosyula S. Somayazulu ,  Srenivas Varadarajan ,  Omesh Tickoo ,  Ibrahima J. Ndiour

IPC分类号： H04N19/52 ,  H04N19/523 ,  G06N3/04 ,  G06K9/62 ,  H04N19/172 ,  G06V10/20

摘要： In one embodiment, an apparatus comprises processing circuitry to: receive, via a communication interface, a compressed video stream captured by a camera, wherein the compressed video stream comprises: a first compressed frame; and a second compressed frame, wherein the second compressed frame is compressed based at least in part on the first compressed frame, and wherein the second compressed frame comprises a plurality of motion vectors; decompress the first compressed frame into a first decompressed frame; perform pixel-domain object detection to detect an object at a first position in the first decompressed frame; and perform compressed-domain object detection to detect the object at a second position in the second compressed frame, wherein the object is detected at the second position in the second compressed frame based on: the first position of the object in the first decompressed frame; and the plurality of motion vectors from the second compressed frame.

公开(公告)号：US20210243012A1

公开(公告)日：2021-08-05

申请号：US16948304

申请日：2020-09-11

申请人： Intel Corporation

发明人： Yen-Kuang Chen ,  Shao-Wen Yang ,  Ibrahima J. Ndiour ,  Yiting Liao ,  Vallabhajosyula S. Somayazulu ,  Omesh Tickoo ,  Srenivas Varadarajan

IPC分类号： H04L9/06 ,  G06F21/64 ,  G06F21/53 ,  G06N5/02 ,  G06K9/00 ,  G06N3/04 ,  H04L29/08 ,  G06F21/45 ,  H04L9/32 ,  H04W4/70 ,  G06F21/44 ,  G06K9/46 ,  G06K9/62 ,  G06F16/538 ,  G06F16/535 ,  G06F16/54 ,  G06F21/62 ,  G06F9/50 ,  G06N3/063 ,  G06N3/08 ,  H04N19/80 ,  G06F16/951 ,  G06K9/36 ,  H04N19/46 ,  G06T7/70 ,  G06K9/64 ,  G06K9/72

摘要： In one embodiment, an apparatus comprises a memory and a processor. The memory is to store visual data associated with a visual representation captured by one or more sensors. The processor is to: obtain the visual data associated with the visual representation captured by the one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data; process the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, wherein the plurality of layers comprises a plurality of filters, and wherein the plurality of filters comprises one or more pixel-domain filters to perform processing associated with uncompressed data and one or more compressed-domain filters to perform processing associated with compressed data; and classify the visual data based on an output of the CNN.

公开(公告)号：US10778412B2

公开(公告)日：2020-09-15

申请号：US16024397

申请日：2018-06-29

申请人： Intel Corporation

发明人： Yen-Kuang Chen ,  Shao-Wen Yang ,  Ibrahima J. Ndiour ,  Yiting Liao ,  Vallabhajosyula S. Somayazulu ,  Omesh Tickoo ,  Srenivas Varadarajan

IPC分类号： H04L9/06 ,  G06F21/64 ,  G06F21/53 ,  G06N5/02 ,  G06K9/00 ,  G06N3/04 ,  H04L29/08 ,  G06F21/45 ,  H04L9/32 ,  H04W4/70 ,  G06F21/44 ,  G06K9/46 ,  G06K9/62 ,  G06N3/08 ,  H04N19/80 ,  G06F16/951 ,  G06K9/36 ,  H04N19/46 ,  G06T7/70 ,  G06K9/64 ,  G06K9/72 ,  H04W12/02 ,  H04N19/42 ,  H04N19/625 ,  H04N19/63 ,  G06T7/223

摘要： In one embodiment, an apparatus comprises a memory and a processor. The memory is to store visual data associated with a visual representation captured by one or more sensors. The processor is to: obtain the visual data associated with the visual representation captured by the one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data; process the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, wherein the plurality of layers comprises a plurality of filters, and wherein the plurality of filters comprises one or more pixel-domain filters to perform processing associated with uncompressed data and one or more compressed-domain filters to perform processing associated with compressed data; and classify the visual data based on an output of the CNN.

公开(公告)号：US10742399B2

公开(公告)日：2020-08-11

申请号：US16024364

申请日：2018-06-29

申请人： Intel Corporation

发明人： Yen-Kuang Chen ,  Shao-Wen Yang ,  Ibrahima J. Ndiour ,  Yiting Liao ,  Vallabhajosyula S. Somayazulu ,  Omesh Tickoo ,  Srenivas Varadarajan

IPC分类号： H04L9/06 ,  H04L29/08 ,  H04L9/32 ,  G06K9/00 ,  G06K9/36 ,  G06K9/62 ,  G06K9/64 ,  G06K9/72 ,  G06K9/46 ,  G06N3/04 ,  G06N3/08 ,  H04N19/46 ,  H04N19/42 ,  H04N19/625 ,  H04N19/63 ,  G06T7/223 ,  G06F21/44 ,  G06F21/64 ,  G06F21/53 ,  G06N5/02 ,  G06F21/45 ,  H04W4/70 ,  H04N19/80 ,  G06F16/951 ,  G06T7/70 ,  H04W12/02

摘要： In one embodiment, an apparatus comprises a storage device and a processor. The storage device may store a plurality of compressed images comprising one or more compressed master images and one or more compressed slave images. The processor may: identify an uncompressed image; access context information associated with the uncompressed image and the one or more compressed master images; determine, based on the context information, whether the uncompressed image is associated with a corresponding master image; upon a determination that the uncompressed image is associated with the corresponding master image, compress the uncompressed image into a corresponding compressed image with reference to the corresponding master image; upon a determination that the uncompressed image is not associated with the corresponding master image, compress the uncompressed image into the corresponding compressed image without reference to the one or more compressed master images; and store the corresponding compressed image on the storage device.

公开(公告)号：US11562181B2

公开(公告)日：2023-01-24

申请号：US16948304

申请日：2020-09-11

申请人： Intel Corporation

发明人： Yen-Kuang Chen ,  Shao-Wen Yang ,  Ibrahima J. Ndiour ,  Yiting Liao ,  Vallabhajosyula S. Somayazulu ,  Omesh Tickoo ,  Srenivas Varadarajan

IPC分类号： G06K9/62 ,  H04L9/06 ,  G06F21/64 ,  G06F21/53 ,  G06N5/02 ,  G06N3/04 ,  G06F21/45 ,  H04L9/32 ,  H04W4/70 ,  G06F21/44 ,  G06F16/538 ,  G06F16/535 ,  G06F16/54 ,  G06F21/62 ,  G06F9/50 ,  G06N3/063 ,  G06V10/20 ,  G06V10/40 ,  G06V10/75 ,  G06V10/44 ,  G06V20/00 ,  G06V40/20 ,  G06V40/16 ,  G06F9/48 ,  H04L67/51 ,  G06T7/11 ,  G06V10/96 ,  G06V30/262 ,  G06K15/02 ,  G06N3/08 ,  H04L67/12 ,  H04N19/80 ,  G06F16/951 ,  H04N19/46 ,  G06T7/70 ,  H04W12/02 ,  H04L9/00 ,  H04N19/12 ,  H04N19/124 ,  H04N19/167 ,  H04N19/172 ,  H04N19/176 ,  H04N19/44 ,  H04N19/48 ,  H04N19/513 ,  G06V30/194 ,  G06T7/20 ,  H04N19/42 ,  H04N19/625 ,  H04N19/63 ,  G06T7/223 ,  H04L67/10

摘要： In one embodiment, an apparatus comprises a memory and a processor. The memory is to store visual data associated with a visual representation captured by one or more sensors. The processor is to: obtain the visual data associated with the visual representation captured by the one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data; process the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, wherein the plurality of layers comprises a plurality of filters, and wherein the plurality of filters comprises one or more pixel-domain filters to perform processing associated with uncompressed data and one or more compressed-domain filters to perform processing associated with compressed data; and classify the visual data based on an output of the CNN.

公开(公告)号：US11166041B2

公开(公告)日：2021-11-02

申请号：US16457802

申请日：2019-06-28

申请人： Intel Corporation

发明人： Yiting Liao ,  Yen-Kuang Chen ,  Shao-Wen Yang ,  Vallabhajosyula S. Somayazulu ,  Srenivas Varadarajan ,  Omesh Tickoo ,  Ibrahima J. Ndiour

IPC分类号： H04N19/52 ,  G06K9/32 ,  G06K9/62 ,  G06N3/04 ,  H04N19/172 ,  H04N19/523

摘要： In one embodiment, an apparatus comprises processing circuitry to: receive, via a communication interface, a compressed video stream captured by a camera, wherein the compressed video stream comprises: a first compressed frame; and a second compressed frame, wherein the second compressed frame is compressed based at least in part on the first compressed frame, and wherein the second compressed frame comprises a plurality of motion vectors; decompress the first compressed frame into a first decompressed frame; perform pixel-domain object detection to detect an object at a first position in the first decompressed frame; and perform compressed-domain object detection to detect the object at a second position in the second compressed frame, wherein the object is detected at the second position in the second compressed frame based on: the first position of the object in the first decompressed frame; and the plurality of motion vectors from the second compressed frame.

公开(公告)号：US20200326667A1

公开(公告)日：2020-10-15

申请号：US16911100

申请日：2020-06-24

申请人： Intel Corporation

发明人： Nilesh Ahuja ,  Ignacio J. Alvarez ,  Ranganath Krishnan ,  Ibrahima J. Ndiour ,  Mahesh Subedar ,  Omesh Tickoo

IPC分类号： G05B13/02 ,  G06N3/08 ,  G06N7/00 ,  G06N5/04 ,  G06K9/62

摘要： Techniques are disclosed for using neural network architectures to estimate predictive uncertainty measures, which quantify how much trust should be placed in the deep neural network (DNN) results. The techniques include measuring reliable uncertainty scores for a neural network, which are widely used in perception and decision-making tasks in automated driving. The uncertainty measurements are made with respect to both model uncertainty and data uncertainty, and may implement Bayesian neural networks or other types of neural networks.

公开(公告)号：US20200250003A1

公开(公告)日：2020-08-06

申请号：US16652038

申请日：2018-06-29

申请人： Intel Corporation

发明人： Shao-Wen Yang ,  Yen-Kuang Chen ,  Ragaad Mohammed Irsehid Altarawneh ,  Juan Pablo Munoz Chiabrando ,  Siew Wen Chin ,  Kushal Datta ,  Subramanya R. Dulloor ,  Julio C. Zamora Esquivel ,  Omar Ulises Florez Choque ,  Vishakha Gupta ,  Scott D. Hahn ,  Rameshkumar Illikkal ,  Nilesh Kumar Jain ,  Siti Khairuni Amalina Kamarol ,  Anil S. Keshavamurthy ,  Heng Kar Lau ,  Jonathan A. Lefman ,  Yiting Liao ,  Michael G. Millsap ,  Ibrahima J. Ndiour ,  Luis Carlos Maria Remis ,  Addicam V. Sanjay ,  Usman Sarwar ,  Eve M. Schooler ,  Ned M. Smith ,  Vallabhajosyula S. Somayazulu ,  Christina R. Strong ,  Omesh Tickoo ,  Srenivas Varadarajan ,  Jesús A. Cruz Vargas ,  Hassnaa Moustafa ,  Arun Raghunath ,  Katalin Klara Bartfai-Walcott ,  Maruti Gupta Hyde ,  Deepak S. Vembar ,  Jessica McCarthy

IPC分类号： G06F9/50 ,  G06K9/00 ,  G06N3/04 ,  G06N3/063

摘要： In one embodiment, an apparatus comprises a processor to: identify a workload comprising a plurality of tasks; generate a workload graph based on the workload, wherein the workload graph comprises information associated with the plurality of tasks; identify a device connectivity graph, wherein the device connectivity graph comprises device connectivity information associated with a plurality of processing devices; identify a privacy policy associated with the workload; identify privacy level information associated with the plurality of processing devices; identify a privacy constraint based on the privacy policy and the privacy level information; and determine a workload schedule, wherein the workload schedule comprises a mapping of the workload onto the plurality of processing devices, and wherein the workload schedule is determined based on the privacy constraint, the workload graph, and the device connectivity graph. The apparatus further comprises a communication interface to send the workload schedule to the plurality of processing devices.