  Deep learning frameworks commonly implement convolution operators with
GEMM-based algorithms. In these algorithms, convolution is implemented on top
of matrix-matrix multiplication (GEMM) functions, provided by highly optimized
BLAS libraries. Convolutions with 1x1 kernels can be directly represented as a
GEMM call, but convolutions with larger kernels require a special memory layout
transformation - im2col or im2row - to fit into GEMM interface.
  The Indirect Convolution algorithm provides the efficiency of the GEMM
primitive without the overhead of im2col transformation. In contrast to
GEMM-based algorithms, the Indirect Convolution does not reshuffle the data to
fit into the GEMM primitive but introduces an indirection buffer - a buffer of
pointers to the start of each row of image pixels. This broadens the
application of our modified GEMM function to convolutions with arbitrary kernel
size, padding, stride, and dilation.
  The Indirect Convolution algorithm reduces memory overhead proportionally to
the number of input channels and outperforms the GEMM-based algorithm by up to
62% on convolution parameters which involve im2col transformations in
GEMM-based algorithms. This, however, comes at cost of minor performance
reduction on 1x1 stride-1 convolutions.
