I found ChannelwiseConvolution is slow in cpu.

[KeyKy]

I use your ChannelwiseConvolution to implement mobilenet. However i only can get (2s/image without mkl, 0.90 with mkl) in cpu while tensorflow-mobilenet is 0.059s/image. So i ask for some idea to improve speed in cpu.

[cypw]

@KeyKy

The running speed of channel wise convolution operation really depends on the parallelization strategy.

My implementation uses BatchGEMM which is slightly faster on very small feature maps (e.g. size=7x7 or size=14x14).

While for larger feature maps (e.g. size=56x56 & size=28x28), I'd recommond you to use the official convolutional layer with option 'num_group = num_filter'.

But still, I don't think it can achieve very high training/testing speed by only using these high-level interfaces. A deeply optimized CUDA code is necessary for fast channel wise convolution. : )

[KeyKy]

@cypw Thanks. I use official convolutional layer with group to deal with larger feature maps. I can get 0.401s/image(cpu) in mxnet with mkl. It can be used in some of my classification task.