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Equation 6 · Part 5 · A History of Small and On-Device AI

Symbol D_K^2

DK⋅DK⋅M⋅DF⋅DF+M⋅N⋅DF⋅DFDK⋅DK⋅M⋅N⋅DF⋅DF=1N+1DK2\frac{D_K \cdot D_K \cdot M \cdot D_F \cdot D_F + M \cdot N \cdot D_F \cdot D_F}{D_K \cdot D_K \cdot M \cdot N \cdot D_F \cdot D_F} = \frac{1}{N} + \frac{1}{D_K^2}
DK2D_K^2

What this part means

DK2D_K^2 occurs below the fraction bar. The quantity above the bar is divided by this expression; zero is excluded as a denominator.

Its job in the formula

DK2D_K^2 occurs below the fraction bar. The quantity above the bar is divided by this expression; zero is excluded as a denominator.

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 DKD_K ×\times DKD_K kernel, M input channels, N output channels and a DFD_F ×\times DFD_F feature map, a standard convolution costs DKD_K ⋅\cdot DKD_K ⋅\cdot M ⋅\cdot N ⋅\cdot DFD_F ⋅\cdot DFD_F multiply-adds. The paper gives the depthwise separable replacement’s…

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Sources cited in the surrounding passage

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