Convolutional neural networks (CNNs) have emerged as one of the most
successful machine learning technologies for image and video processing. The
most computationally intensive parts of CNNs are the convolutional layers,
which convolve multi-channel images with multiple kernels. A common approach to
implementing convolutional layers is to expand the image into a column matrix
(im2col) and perform Multiple Channel Multiple Kernel (MCMK) convolution using
an existing parallel General Matrix Multiplication (GEMM) library. This im2col
conversion greatly increases the memory footprint of the input matrix and
reduces data locality.
In this paper we propose a new approach to MCMK convolution that is based on
General Matrix Multiplication (GEMM), but not on im2col. Our algorithm
eliminates the need for data replication on the input thereby enabling us to
apply the convolution kernels on the input images directly. We have implemented
several variants of our algorithm on a CPU processor and an embedded ARM
processor. On the CPU, our algorithm is faster than im2col in most cases.

本文提出了一种新的 MCMK 卷积方法，能够在不使用 im2col 的情况下将卷积核应用于输入图像，相比 im2col 方法，该方法在 CPU 上的速度更快。