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The passage around this formula

MobileNet, published by Howard and colleagues at Google fourteen months later, generalized the same idea into a reusable building block rather than one bespoke network. A standard convolutional layer filters and combines its inputs in a single step; MobileNet’s depthwise separable convolution splits that into a depthwise layer that filters each input channel on its own, followed by a 1×1 pointwise convolution that recombines the results [ 1 ] . For a $D_K$ $\times$ $D_K$ kernel, M input channels, N output channels and a $D_F$ $\times$ $D_F$ feature map, a standard convolution costs $D_K$ $\cdot$ $D_K$ $\cdot$ M $\cdot$ N $\cdot$ $D_F$ $\cdot$ $D_F$ multiply-adds. The paper gives the depthwise separable replacement’s…

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