shufflenet units

**ShuffleNet Units** are **building blocks of the ShuffleNet architecture that use channel shuffle operations between grouped convolutions** — solving the information isolation problem of grouped convolutions by rearranging channels so each group receives input from all previous groups. **How Do ShuffleNet Units Work?** - **Grouped 1×1 Conv**: Reduce channels via grouped pointwise convolution (efficient but isolates groups). - **Channel Shuffle**: Reshape channels into $(G, C/G)$ -> transpose -> flatten. Now each group has channels from all original groups. - **Depthwise Conv**: 3×3 depthwise convolution for spatial processing. - **Grouped 1×1 Conv**: Another grouped pointwise to recover channel dimension. - **Paper**: Zhang et al. (2018). **Why It Matters** - **Cross-Group Communication**: Channel shuffle bridges the information gap between groups without the cost of full 1×1 convolution. - **Extreme Efficiency**: Designed for <100 MFLOPs models (smartwatch, IoT devices). - **v2**: ShuffleNetV2 further optimizes for actual inference speed (not just FLOPs). **ShuffleNet Units** are **efficient blocks with channel shuffling** — solving grouped convolution's isolation problem with a zero-computation rearrangement.