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公开(公告)号：EP3314544A1
公开(公告)日：2018-05-02
申请号：EP16738286.0
申请日：2016-06-24
CPC分类号： G06N3/04 , G06N3/0454 , G06N3/063
摘要： A method is provided for implementing a deep neural network on a server component that includes a host component including a CPU and a hardware acceleration component coupled to the host component. The deep neural network includes a plurality of layers. The method includes partitioning the deep neural network into a first segment and a second segment, the first segment including a first subset of the plurality of layers, the second segment including a second subset of the plurality of layers, configuring the host component to implement the first segment, and configuring the hardware acceleration component to implement the second segment.
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公开(公告)号：EP3314542A1
公开(公告)日：2018-05-02
申请号：EP16735802.7
申请日：2016-06-27
CPC分类号： G06N3/063 , G06F15/7803 , G06N3/04 , G06N3/0454
摘要： A hardware acceleration component is provided for implementing a convolutional neural network. The hardware acceleration component includes an array of N rows and M columns of functional units, an array of N input data buffers configured to store input data, and an array of M weights data buffers configured to store weights data. Each of the N input data buffers is coupled to a corresponding one of the N rows of functional units. Each of the M weights data buffers is coupled to a corresponding one of the M columns of functional units. Each functional unit in a row is configured to receive a same set of input data. Each functional unit in a column is configured to receive a same set of weights data from the weights data buffer coupled to the row. Each of the functional units is configured to perform a convolution of the received input data and the received weights data, and the M columns of functional units are configured to provide M planes of output data.
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