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公开(公告)号：US11875555B2
公开(公告)日：2024-01-16
申请号：US17534558
申请日：2021-11-24
Applicant: Intel Corporation
Inventor： Anirud Thyagharajan , Prashant Laddha , Benjamin Ummenhofer , Om Ji Omer
IPC: G06V10/774 , G06V10/77 , G06V10/776
CPC classification number: G06V10/7753 , G06V10/776 , G06V10/7715
Abstract: A computer model is trained to classify regions of a space (e.g., a pixel of an image or a voxel of a point cloud) according to a multi-label classification. To improve the model's accuracy, the model's self-confidence is determined with respect to its own predictions of regions in a training space. The self-confidence is determined based on the class predictions, such as a difference between the highest-predicted class and a second-highest-predicted class. When these are similar, it may reflect areas for potential improvement by focusing training on these low-confidence areas. Additional training may be performed by including modified training data in subsequent training iterations that focuses on low-confidence areas. As another example, additional training may be performed using the self-confidence to modify a classification loss used to refine parameters of the model.
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公开(公告)号：US20220084310A1
公开(公告)日：2022-03-17
申请号：US17534558
申请日：2021-11-24
Applicant: Intel Corporation
Inventor： Anirud Thyagharajan , Prashant Laddha , Benjamin Ummenhofer , Om Ji Omer
IPC: G06V10/774 , G06V10/776 , G06V10/77
Abstract: A computer model is trained to classify regions of a space (e.g., a pixel of an image or a voxel of a point cloud) according to a multi-label classification. To improve the model's accuracy, the model's self-confidence is determined with respect to its own predictions of regions in a training space. The self-confidence is determined based on the class predictions, such as a difference between the highest-predicted class and a second-highest-predicted class. When these are similar, it may reflect areas for potential improvement by focusing training on these low-confidence areas. Additional training may be performed by including modified training data in subsequent training iterations that focuses on low-confidence areas. As another example, additional training may be performed using the self-confidence to modify a classification loss used to refine parameters of the model.
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公开(公告)号：US20230169319A1
公开(公告)日：2023-06-01
申请号：US18159555
申请日：2023-01-25
Applicant: INTEL CORPORATION
Inventor： Kamlesh Pillai , Gurpreet Singh Kalsi , Sreenivas Subramoney , Prashant Laddha , Om Ji Omer
CPC classification number: G06N3/063 , G06F7/5443 , G06N3/04 , G06T1/60 , G06V10/955 , G06V20/64 , G06F18/253 , G06F18/2136
Abstract: Systems, apparatuses and methods may provide for technology that decodes data via an instruction that indicates a number of rulebooks to be processed, an input feature size, an output feature size, and a plurality of feature map base addresses, rearranges spatially distributed voxel output feature maps in the decoded data based on weight planes, and performs a channel-wise multiply-accumulate (MAC) operation on the rearranged spatially distributed voxel output feature maps to obtain an output, wherein the channel-wise MAC operation is performed as partial accumulations by a plurality of processing elements.
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公开(公告)号：US20190333183A1
公开(公告)日：2019-10-31
申请号：US16449896
申请日：2019-06-24
Applicant: Intel Corporation
Inventor： Gopi Neela , Dipan Kumar Mandal , Gurpreet S. Kalsi , Prashant Laddha , Om J. Omer , Anirud Thyagharajan , Srivatsava Jandhyala
Abstract: An embodiment of an image processor device includes technology to fetch a feature point data set from outside a local memory, locally store three or more fetched feature point data sets in the local memory, compute orientation information for each fetched feature point data set, compute first descriptor information based on the computed orientation information and a first locally stored feature point data set in parallel with a fetch and local store of a second feature point data set in the local memory, and compute second descriptor information based on the computed orientation information and the second locally stored feature point data set in parallel with the compute of the first descriptor information. Other embodiments are disclosed and claimed.
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公开(公告)号：US20190171909A1
公开(公告)日：2019-06-06
申请号：US16232778
申请日：2018-12-26
Applicant: INTEL CORPORATION
Inventor： Dipan Kumar Mandal , Gurpreet Kalsi , Om J. Omer , Prashant Laddha , Sreenivas Subramoney
Abstract: An example apparatus for selecting keypoints in image includes a keypoint detector to detect keypoints in a plurality of received images. The apparatus also includes a score calculator to calculate a keypoint score for each of the detected keypoints based on a descriptor score indicating descriptor invariance. The apparatus includes a keypoint selector to select keypoints based on the calculated keypoint scores. The apparatus also further includes a descriptor calculator to calculate descriptors for each of the selected keypoints. The apparatus also includes a descriptor matcher to match corresponding descriptors between images in the plurality of received images. The apparatus further also includes a feature tracker to track a feature in the plurality of images based on the matched descriptors.
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Patent Agency Ranking
公开(公告)号：US20250021819A1
公开(公告)日：2025-01-16
申请号：US18900006
申请日：2024-09-27
Applicant: Intel Corporation
Inventor： Vinay Joshi , Om Ji Omer , Prashant Laddha , Shambhavi Sinha
IPC: G06N3/086
Abstract: Systems, apparatus, articles of manufacture, and methods for quality and capacity-aware grouped query attention are disclosed. To accomplish such groupings, example instructions cause a machine to create a plurality of groups of query heads present in a key value cache using an evolutionary algorithm based on at least two objectives, quantify an amount of error introduced by a first group of query heads in the plurality of groups of query heads, and retain the query heads of the first group of query heads in a non-grouped arrangement when the error meets an error threshold.
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公开(公告)号：US12189559B2
公开(公告)日：2025-01-07
申请号：US16913370
申请日：2020-06-26
Applicant: Intel Corporation
Inventor： Anirud Thyagharajan , Prashant Laddha , Om Omer , Sreenivas Subramoney
Abstract: Exemplary embodiments maintain spatial locality of the data being processed by a sparse CNN. The spatial locality is maintained by reordering the data to preserve spatial locality. The reordering may be performed on data elements and on data for groups of co-located data elements referred to herein as “chunks”. Thus, the data may be reordered into chunks, where each chunk contains data for spatially co-located data elements, and in addition, chunks may be organized so that spatially located chunks are together. The use of chunks helps to reduce the need to re-fetch data during processing. Chunk sizes may be chosen based on the memory constraints of the processing logic (e.g., cache sizes).
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公开(公告)号：US20230161626A1
公开(公告)日：2023-05-25
申请号：US18049509
申请日：2022-10-25
Applicant: Intel Corporation
Inventor： Gurpreet S. Kalsi , Om Ji Omer , Prashant Laddha , Kamlesh R. Pillai , Anirud Thyagharajan , Meenal Kudalkar , Krishnan Ananthanarayanan , Sreenivas Subramoney
CPC classification number: G06F9/5027 , G06T1/60
Abstract: An embodiment of an apparatus comprises a hardware accelerator to perform a three-dimensional (3D) point cloud data access operation, and circuitry coupled to the hardware accelerator to control the hardware accelerator to perform the 3D point cloud data access operation in response to a request. Other embodiments are disclosed and claimed.
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公开(公告)号：US11238309B2
公开(公告)日：2022-02-01
申请号：US16232778
申请日：2018-12-26
Applicant: INTEL CORPORATION
Inventor： Dipan Kumar Mandal , Gurpreet Kalsi , Om J Omer , Prashant Laddha , Sreenivas Subramoney
Abstract: An example apparatus for selecting keypoints in image includes a keypoint detector to detect keypoints in a plurality of received images. The apparatus also includes a score calculator to calculate a keypoint score for each of the detected keypoints based on a descriptor score indicating descriptor invariance. The apparatus includes a keypoint selector to select keypoints based on the calculated keypoint scores. The apparatus also further includes a descriptor calculator to calculate descriptors for each of the selected keypoints. The apparatus also includes a descriptor matcher to match corresponding descriptors between images in the plurality of received images. The apparatus further also includes a feature tracker to track a feature in the plurality of images based on the matched descriptors.
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公开(公告)号：US20210110187A1
公开(公告)日：2021-04-15
申请号：US17131121
申请日：2020-12-22
Applicant: Intel Corporation
Inventor： Kamlesh Pillai , Gurpreet Singh Kalsi , Sreenivas Subramoney , Prashant Laddha , Om Ji Omer
Abstract: Systems, apparatuses and methods may provide for technology that decodes data via an instruction that indicates a number of rulebooks to be processed, an input feature size, an output feature size, and a plurality of feature map base addresses, rearranges spatially distributed voxel output feature maps in the decoded data based on weight planes, and performs a channel-wise multiply-accumulate (MAC) operation on the rearranged spatially distributed voxel output feature maps to obtain an output, wherein the channel-wise MAC operation is performed as partial accumulations by a plurality of processing elements.
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