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EFFICIENT CONVOLUTION OF MULTI-CHANNEL INPUT SAMPLES WITH MULTIPLE KERNELS
EFFICIENT CONVOLUTION OF MULTI-CHANNEL INPUT SAMPLES WITH MULTIPLE KERNELS
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机译:具有多个内核的多通道输入样本的高效卷积
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摘要
Convolutions of an input sample with multiple kernels is decomposed into matrix multiplications of a V×C matrix of input values times a C×K matrix of kernel values, producing a V×K product. For the second matrix, C is a channel dimension (i.e., each row of the second matrix is a different channel of the input sample and kernel) and K is the kernel dimension (i.e., each column of the second matrix is a different kernel), but all the values correspond to the same pixel position in the kernel. In the matrix product, V is the output dimension and K is the kernel dimension. Thus, each value in the output matrix is a partial product for a certain output pixel and kernel, and the matrix multiplication parallelizes the convolutions by calculating partial products for multiple output pixels and multiple kernels.
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