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The convolutional layers are most computationally intense parts of Convolutional neural networks (CNNs).Currently the common approach to impement convolutional layers is to expand the image into a column matrix(im2col) and perform and perform Multiple Channel Multiple Kernel (MCMK) convolution using an existing parallel General Matrix Multiplication (GEMM) library. However im2col operation need load and store the image data, and also need another memory block to hold the intermediate data.

If I need to optimize the convolutional implementation, I may choose to direct implementation with SIMD instructions. Such method will not incur any memory operation overhead.

the benefits from the very regular patterns of memory access outweigh the wasteful storage costs.

From the following link, at the end of the link

https://petewarden.com/2015/04/20/why-gemm-is-at-the-heart-of-deep-learning/

So I hope to know the reason. May floating point operations require more instruction cycle? or the input image is not much large, so it may residue in the cache and the memory operations don't need access DDR and consume less cycles.

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