Depthwise Separable Convolution — a factorized convolution that dramatically reduces computation and parameters by splitting a standard convolution into two steps, enabling efficient mobile and edge deployment.

Standard Convolution

• Input: $H \times W \times C_{in}$ → Output: $H \times W \times C_{out}$
• One filter: $K \times K \times C_{in}$ (mixes spatial AND channel info simultaneously)
• Cost: $K^2 \times C_{in} \times C_{out} \times H \times W$

Depthwise Separable (Two Steps)

1. Depthwise Conv: One $K \times K$ filter per input channel (spatial only, no channel mixing). Cost: $K^2 \times C_{in} \times H \times W$ 2. Pointwise Conv: $1 \times 1$ convolution to mix channels. Cost: $C_{in} \times C_{out} \times H \times W$

Savings

• Reduction factor: $\frac{1}{C_{out}} + \frac{1}{K^2}$
• For 3x3 conv with 256 output channels: ~8-9x fewer operations

Key Architectures

• MobileNetV1/V2/V3: Google's mobile-optimized CNNs using depthwise separable convolutions
• EfficientNet: NAS-designed architecture using similar factorization
• Xception: "Extreme Inception" — replaced all convolutions with depthwise separable

Depthwise separable convolutions make it possible to run powerful vision models on smartphones and IoT devices in real-time.