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公开(公告)号：US10248562B2
公开(公告)日：2019-04-02
申请号：US15640349
申请日：2017-06-30
摘要： In an embodiment, a partition cost of one or more of the plurality of partitions and a data block cost for one or more data blocks that may be subjected to a garbage collection operation are determined. The partition cost and the data block cost are combined into an overall reclaim cost by specifying both the partition cost and the data block cost in terms of a computing system latency. A byte constant multiplier that is configured to modify the overall reclaim cost to account for the amount of data objects that may be rewritten during the garbage collection operation may be applied. The one or more partitions and/or one or more data blocks that have the lowest overall reclaim cost while reclaiming an acceptable amount of data block space may be determined and be included in a garbage collection schedule.
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公开(公告)号：US10223604B2
公开(公告)日：2019-03-05
申请号：US15373301
申请日：2016-12-08
IPC分类号： G06K9/00 , G06F9/48 , G06F9/50 , H04N7/18 , H04N21/234
摘要： Various technologies described herein pertain to performing video analytics. The approaches set forth herein support live video analytics at scale with approximate and delay-tolerant processing. Video streams can be captured by multiple cameras and continuously streamed to a video analytics computing system; the video streams can be received at the video analytics computing system. Multiple video analytics queries can be executed on the video streams. The multiple video analytics queries can be concurrently executed by the video analytics computing system on the video streams as the video streams are continuously streamed to the video analytics computing system. The multiple video analytics queries can be executed utilizing resources of the video analytics computing system allocated between the multiple video analytics queries. Execution of the multiple video analytics queries can return respective results for the multiple video analytics queries. The results for the multiple video analytics queries can be outputted.
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公开(公告)号：US10685235B2
公开(公告)日：2020-06-16
申请号：US15971563
申请日：2018-05-04
摘要： A method can include classifying, using a compressed and specialized convolutional neural network (CNN), an object of a video frame into classes, clustering the object based on a distance of a feature vector of the object to a feature vector of a centroid object of the cluster, storing top-k classes, a centroid identification, and a cluster identification, in response to receiving a query for objects of class X from a specific video stream, retrieving image data for each centroid of each cluster that includes the class X as one of the top-k classes, classifying, using a ground truth CNN (GT-CNN), the retrieved image data for each centroid, and for each centroid determined to be classified as a member of the class X providing image data for each object in each cluster associated with the centroid.
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公开(公告)号：US20180129892A1
公开(公告)日：2018-05-10
申请号：US15373301
申请日：2016-12-08
IPC分类号： G06K9/00
CPC分类号： G06K9/00979 , G06F9/4881 , G06F9/50 , G06K9/00711 , G06K9/00771 , G06K9/00785 , G06K9/00993 , G06K2009/00322 , G06K2009/00738 , H04N7/181 , H04N21/23418
摘要： Various technologies described herein pertain to performing video analytics. The approaches set forth herein support live video analytics at scale with approximate and delay-tolerant processing. Video streams can be captured by multiple cameras and continuously streamed to a video analytics computing system; the video streams can be received at the video analytics computing system. Multiple video analytics queries can be executed on the video streams. The multiple video analytics queries can be concurrently executed by the video analytics computing system on the video streams as the video streams are continuously streamed to the video analytics computing system. The multiple video analytics queries can be executed utilizing resources of the video analytics computing system allocated between the multiple video analytics queries. Execution of the multiple video analytics queries can return respective results for the multiple video analytics queries. The results for the multiple video analytics queries can be outputted.
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5.发明申请公开(公告)号：US20170149691A1
公开(公告)日：2017-05-25
申请号：US14948217
申请日：2015-11-20
发明人： Paramvir Bahl , Ganesh Ananthanarayanan , Srikanth Kandula , Peter Bodik , Qifan Pu , Srinivasa Aditya Akella
IPC分类号： H04L12/911 , H04L12/26
CPC分类号： H04L47/823 , H04L41/147 , H04L43/0852 , H04L43/16
摘要： Latency in responding to queries directed to geographically distributed data can be reduced by allocating individual steps, of a multi-step compute operation requested by the query, among the geographically distributed computing devices so as to reduce the duration of shuffling of intermediate data among such devices, and, additionally, by pre-moving, prior to the receipt of the query, portions of the distributed data that are input to a first step of the multistep compute operation, to, again, reduce the duration of the exchange of intermediate data. The pre-moving of input data occurring, and the adaptive allocation of intermediate steps, are prioritized for high-value data sets. Additionally, a threshold increase in a quantity of data exchanged across network communications can be established to avoid incurring network communication usage without an attendant gain in latency reduction.
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6.发明申请公开(公告)号：US20190273695A1
公开(公告)日：2019-09-05
申请号：US16419275
申请日：2019-05-22
发明人： Paramvir Bahl , Ganesh Ananthanarayanan , Srikanth Kandula , Peter Bodik , Qifan Pu , Srinivasa Aditya Akella
IPC分类号： H04L12/911 , H04L12/24 , H04L12/26
摘要： Latency in responding to queries directed to geographically distributed data can be reduced by allocating individual steps, of a multi-step compute operation requested by the query, among the geographically distributed computing devices so as to reduce the duration of shuffling of intermediate data among such devices, and, additionally, by pre-moving, prior to the receipt of the query, portions of the distributed data that are input to a first step of the multistep compute operation, to, again, reduce the duration of the exchange of intermediate data. The pre-moving of input data occurring, and the adaptive allocation of intermediate steps, are prioritized for high-value data sets. Additionally, a threshold increase in a quantity of data exchanged across network communications can be established to avoid incurring network communication usage without an attendant gain in latency reduction.
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7.发明授权公开(公告)号：US10320708B2
公开(公告)日：2019-06-11
申请号：US14948217
申请日：2015-11-20
发明人： Paramvir Bahl , Ganesh Ananthanarayanan , Srikanth Kandula , Peter Bodik , Qifan Pu , Srinivasa Aditya Akella
IPC分类号： H04L12/24 , H04L12/26 , H04L12/911
摘要： Latency in responding to queries directed to geographically distributed data can be reduced by allocating individual steps, of a multi-step compute operation requested by the query, among the geographically distributed computing devices so as to reduce the duration of shuffling of intermediate data among such devices, and, additionally, by pre-moving, prior to the receipt of the query, portions of the distributed data that are input to a first step of the multistep compute operation, to, again, reduce the duration of the exchange of intermediate data. The pre-moving of input data occurring, and the adaptive allocation of intermediate steps, are prioritized for high-value data sets. Additionally, a threshold increase in a quantity of data exchanged across network communications can be established to avoid incurring network communication usage without an attendant gain in latency reduction.
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公开(公告)号：US11354902B2
公开(公告)日：2022-06-07
申请号：US16875080
申请日：2020-05-15
摘要： A method can include classifying, using a compressed and specialized convolutional neural network (CNN), an object of a video frame into classes, clustering the object based on a distance of a feature vector of the object to a feature vector of a centroid object of the cluster, storing top-k classes, a centroid identification, and a cluster identification, in response to receiving a query for objects of class X from a specific video stream, retrieving image data for each centroid of each cluster that includes the class X as one of the top-k classes, classifying, using a ground truth CNN (GT-CNN), the retrieved image data for each centroid, and for each centroid determined to be classified as a member of the class X providing image data for each object in each cluster associated with the centroid.
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公开(公告)号：US10747665B2
公开(公告)日：2020-08-18
申请号：US16371811
申请日：2019-04-01
摘要： In an embodiment, a partition cost of one or more of the plurality of partitions and a data block cost for one or more data blocks that may be subjected to a garbage collection operation are determined. The partition cost and the data block cost are combined into an overall reclaim cost by specifying both the partition cost and the data block cost in terms of a computing system latency. A byte constant multiplier that is configured to modify the overall reclaim cost to account for the amount of data objects that may be rewritten during the garbage collection operation may be applied. The one or more partitions and/or one or more data blocks that have the lowest overall reclaim cost while reclaiming an acceptable amount of data block space may be determined and be included in a garbage collection schedule.
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公开(公告)号：US20190205649A1
公开(公告)日：2019-07-04
申请号：US15971563
申请日：2018-05-04
CPC分类号： G06K9/00718 , G06K9/00711 , G06K9/6218 , G06K9/6227 , G06K9/6256 , G06K9/6269
摘要： A method can include classifying, using a compressed and specialized convolutional neural network (CNN), an object of a video frame into classes, clustering the object based on a distance of a feature vector of the object to a feature vector of a centroid object of the cluster, storing top-k classes, a centroid identification, and a cluster identification, in response to receiving a query for objects of class X from a specific video stream, retrieving image data for each centroid of each cluster that includes the class X as one of the top-k classes, classifying, using a ground truth CNN (GT-CNN), the retrieved image data for each centroid, and for each centroid determined to be classified as a member of the class X providing image data for each object in each cluster associated with the centroid.
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