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Published equation contexts

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}

Why this formula appears here

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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DK2D_K^2

Symbol D_K^2

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

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DK⋅DK⋅M⋅DF⋅DF+M⋅N⋅DF⋅DFD_K \cdot D_K \cdot M \cdot D_F \cdot D_F + M \cdot N \cdot D_F \cdot D_F

Numerator: D_K × D_K × M × D_F × D_F + M × N × D_F × D_F

The complete quantity above the fraction bar.

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DK⋅DK⋅M⋅N⋅DF⋅DFD_K \cdot D_K \cdot M \cdot N \cdot D_F \cdot D_F

Denominator: D_K × D_K × M × N × D_F × D_F

The complete quantity below the fraction bar; it must be nonzero for this division.

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How to interpret it

With a fixed numerator, increasing a nonzero denominator reduces the fraction. Read it with the definitions, units, and assumptions supplied by the article.

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Published contexts (1)

A symbol can carry a different meaning in another article. Each occurrence keeps its own guide and term definitions.

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}

Equation 6 · Edge AI & Electronics

A History of Small and On-Device AI

This equation states an equality: the expressions on both sides have the same value under the article’s assumptions.

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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