You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: Thanks to pointwise group convolution with channel shuffle, all components in ShuffleNet unit can be computed efficiently. Compared with ResNet [9] (bottleneck design) and ResNeXt [40], our structure has less complexity under the same settings. For example, given the input size c\times h\times w and the bottleneck channels m, ResNet unit requires hw(2cm+9m^{2}) FLOPs and ResNeXt has hw(2cm+9m^{2}/g) FLOPs, while our ShuffleNet unit requires only hw(2cm/g+9m) FLOPs, where g means the number of groups for convolutions. In other words, given a computational budget, ShuffleNet can use wider feature maps. We find this is critical for small networks, as tiny networks usually have an insufficient number of channels to process the information.

Question: Given input size (3x224x224) and bottleneck channels being 64, compare the computational complexity between ResNet, ResNext and ShuffleNet.
ResNet: hw(2cm+9m^{2}) = 244^2*2 * 3 * 64 + 244^2*9 * 64^2 = 2 217 596 928
ResNeXt: hw(2cm+9m^{2}/g) = 244^2*2 * 3 * 64 + 244^2*9 * 64^2/g = 22 861 824 + 2 194 735 104 / g
ShuffleNet: hw(2cm/g + 9m) = 244^2*2 * 3 * 64 / g + 244^2*9 * 64 = 22 861 824 / g + 34 292 736
Even with the group size of 1 (g=1), ShuffleNet have much less complexity compared to ResNet and ResNeXt.