Grouped Convolution

Grouped Convolution is a convolution where input channels are divided into $G$ groups, and each group is convolved independently — reducing parameters and FLOPs by a factor of $G$ while processing different channel subsets separately.

How Does Grouped Convolution Work?

• Split: Divide $C_{in}$ input channels into $G$ groups of $C_{in}/G$ channels each.
• Convolve: Each group is convolved with its own set of filters independently.
• Concatenate: Concatenate the $G$ group outputs along the channel dimension.
• Special Cases: $G = 1$ (standard conv), $G = C_{in}$ (depthwise conv).

Why It Matters

• AlexNet Origin: Originally introduced in AlexNet (2012) to split computation across two GPUs.
• Efficiency: Reduces parameters and FLOPs by factor $G$ compared to standard convolution.
• ResNeXt: ResNeXt uses 32 groups as a design principle ("cardinality"), showing grouped conv improves accuracy.

Grouped Convolution is parallel independent convolutions — splitting channels into groups for efficient, parallelizable feature extraction.