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CONVOLUTION MATRIX MULTIPLY WITH CALLBACK FOR DEEP TILING FOR DEEP CONVOLUTIONAL NEURAL NETWORKS

机译:深层卷积矩阵与回卷相乘,用于深层卷积神经网络

摘要

A method of address translation of images and filters to virtual matrices to perform a convolution by matrix multiplication includes receiving an image and a filter. Each image and filter has a memory address. The method also includes mapping the memory addresses to virtual matrix addresses based on a calculated linearized image and a calculated linearized filter. The method further includes converting data in the virtual matrix to a predefined internal format. The method still further includes convolving the image by matrix multiplication of the data in the predefined internal format based on the virtual matrix addresses. REFER TO FIGURE 10B
机译:一种将图像和滤波器的地址转换为虚拟矩阵以通过矩阵乘法执行卷积的方法,包括接收图像和滤波器。每个图像和滤镜都有一个内存地址。该方法还包括基于计算的线性化图像和计算的线性化滤波器将存储器地址映射到虚拟矩阵地址。该方法还包括将虚拟矩阵中的数据转换为预定的内部格式。该方法还包括基于虚拟矩阵地址通过以预定义的内部格式对数据进行矩阵乘法来对图像进行卷积。参考图10B

著录项

  • 公开/公告号IN201747022638A

    专利类型

  • 公开/公告日2017-07-07

    原文格式PDF

  • 申请/专利权人

    申请/专利号IN201747022638

  • 申请日2017-06-28

  • 分类号G06N3/04;

  • 国家 IN

  • 入库时间 2022-08-21 13:38:12

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