Depthwise-separable convolutions: how MobileNet gets ~8-9x fewer multiplies by splitting one conv into two
Article summary
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A standard convolution quietly does two very different jobs at once. It filters space — a Dk×Dk window looks at local pattern — and it mixes channels — it sums across all Cin inputs to build each of Cout outputs. Because those are fused into one fat Dk×Dk×Cin×Cout tensor, the cost is Dk²·Cin·Cout·Df² multiply-adds, quadratic in both channel count and kernel size. On a phone, stacks of these are the bottleneck. MobileNet asks: do we really need to filter space and mix channels in the same…
1Key Takeaways
- A standard convolution quietly does two very different jobs at once.
- It filters space — a Dk×Dk window looks at local pattern — and it mixes channels — it sums across all Cin inputs to build each of Cout outputs.
- Because those are fused into one fat Dk×Dk×Cin×Cout tensor, the cost is Dk²·Cin·Cout·Df² multiply-adds, quadratic in both channel count and kernel size.
- On a phone, stacks of these are the bottleneck.
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that a standard convolution quietly does two very different jobs at once.
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