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公开(公告)号：EP4160448A1
公开(公告)日：2023-04-05
申请号：EP22194202.2
申请日：2016-12-29
申请人： Google LLC
摘要： Methods, systems, and apparatus, including a system for transforming sparse elements to a dense matrix. The system is configured to receive a request for an output matrix based on sparse elements including sparse elements associated with a first dense matrix and sparse elements associated with a second dense matrix; obtain the sparse elements associated with the first dense matrix fetched by a first group of sparse element access units; obtain the sparse elements associated with the second dense matrix fetched by a second group of sparse element access units; and transform the sparse elements associated with the first dense matrix and the sparse elements associated with the second dense matrix to generate the output dense matrix that includes the sparse elements associated with the first dense matrix and the sparse elements associated with the second dense matrix.
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公开(公告)号：EP4022446A1
公开(公告)日：2022-07-06
申请号：EP20765412.0
申请日：2020-08-19
申请人： Google LLC
发明人： GUPTA, Suyog , NARAYANASWAMI, Ravi , DASARI, Uday Kumar , IRANLI, Ali , THIRUNAGARI, Pavan , KUMAR, Vinu Vijay , KOSIREDDY, Sunitha R.
IPC分类号： G06F12/084 , G06F12/0846 , G06F12/0895 , G06F12/0893
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公开(公告)号：EP4254313A3
公开(公告)日：2023-12-20
申请号：EP23187356.3
申请日：2019-01-30
申请人： Google LLC
IPC分类号： G06T1/60
摘要： Methods, systems, and apparatus, including an apparatus for determining pixel coordinates for image transformation and memory addresses for storing the transformed image data. In some implementations, a system includes a processing unit configured to perform machine learning computations for images using a machine learning model and pixel values for the images, a storage medium configured to store the pixel values, and a memory address computation unit that includes one or more hardware processors. The processors are configured to receive image data for an image and determine that the dimensions of the image do not match the dimensions of the machine learning model. In response, the processors determine pixel coordinates for a transformed version of the image and, for each of the pixel coordinates, memory address(es), in the storage medium, for storing pixel value(s) that will be used to generate an input to the machine learning model.
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6.发明公开
公开(公告)号：EP4273755A2
公开(公告)日：2023-11-08
申请号：EP23182760.1
申请日：2018-09-21
申请人： Google LLC
IPC分类号： G06N3/04
摘要： Apparatus and methods for processing neural network models are provided. The apparatus can comprise a plurality of identical artificial intelligence processing dies. Each artificial intelligence processing die among the plurality of identical artificial intelligence processing dies can include at least one inter-die input block and at least one inter-die output block. Each artificial intelligence processing die among the plurality of identical artificial intelligence processing dies is communicatively coupled to another artificial intelligence processing die among the plurality of identical artificial intelligence processing dies by way of one or more communication paths from the at least one inter-die output block of the artificial intelligence processing die to the at least one inter-die input block of the artificial intelligence processing die. Each artificial intelligence processing die among the plurality of identical artificial intelligence processing dies corresponds to at least one layer of a neural network.
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公开(公告)号：EP4235509A2
公开(公告)日：2023-08-30
申请号：EP23161136.9
申请日：2017-08-29
申请人： Google LLC
摘要： A computer-implemented method that includes receiving, by a processing unit, an instruction that specifies data values for performing a tensor computation. In response to receiving the instruction, the method may include, performing, by the processing unit, the tensor computation by executing a loop nest comprising a plurality of loops, wherein a structure of the loop nest is defined based on one or more of the data values of the instruction. The tensor computation can be at least a portion of a computation of a neural network layer. The data values specified by the instruction may comprise a value that specifies a type of the neural network layer, and the structure of the loop nest can be defined at least in part by the type of the neural network layer.
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公开(公告)号：EP4004730A1
公开(公告)日：2022-06-01
申请号：EP20800389.7
申请日：2020-10-07
申请人： Google LLC
IPC分类号： G06F9/50
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公开(公告)号：EP3782020A1
公开(公告)日：2021-02-24
申请号：EP19795733.5
申请日：2019-10-11
申请人： Google LLC
IPC分类号： G06F7/485
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公开(公告)号：EP4235509A3
公开(公告)日：2023-09-20
申请号：EP23161136.9
申请日：2017-08-29
申请人： Google LLC
IPC分类号： G06N3/04 , G06N3/063 , G06N3/0464
摘要： A computer-implemented method that includes receiving, by a processing unit, an instruction that specifies data values for performing a tensor computation. In response to receiving the instruction, the method may include, performing, by the processing unit, the tensor computation by executing a loop nest comprising a plurality of loops, wherein a structure of the loop nest is defined based on one or more of the data values of the instruction. The tensor computation can be at least a portion of a computation of a neural network layer. The data values specified by the instruction may comprise a value that specifies a type of the neural network layer, and the structure of the loop nest can be defined at least in part by the type of the neural network layer.
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