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Context: We notice that state-of-the-art basic architectures such as Xception [3] and ResNeXt [40] become less efficient in extremely small networks because of the costly dense 1\times 1 convolutions. We propose using pointwise group convolutions to reduce computation complexity of 1\times 1 convolutions. To overcome the side effects brought by group convolutions, we come up with a novel channel shuffle operation to help the information flowing across feature channels. Based on the two techniques, we build a highly efficient architecture called ShuffleNet. Compared with popular structures like  [30, 9, 40], for a given computation complexity budget, our ShuffleNet allows more feature map channels, which helps to encode more information and is especially critical to the performance of very small networks. In addition, in ShuffleNet depthwise convolution only performs on bottleneck feature maps. Even though depthwise convolution usually has very low theoretical complexity, we find it difficult to efficiently implement on low-power mobile devices, which may result from a worse computation/memory access ratio compared with other dense operations. Such drawback is also referred in  [3], which has a runtime library based on TensorFlow [1]. In ShuffleNet units, we intentionally use depthwise convolution only on bottleneck in order to prevent overhead as much as possible. Recent leading convolutional units in VGG [30], ResNet [9], GoogleNet [33], ResNeXt [40] and Xception [3] have pursued state-of-the-art results with large models (e.g. \geq 1GFLOPs), but do not fully explore low-complexity conditions. In this section we survey a variety of building blocks and make comparisons with ShuffleNet under the same complexity constraint. Recently Howard et al. have proposed MobileNets [12] which mainly focus on efficient network architecture for mobile devices. MobileNet takes the idea of depthwise separable convolution from  [3] and achieves state-of-the-art results on small models. The concept of group convolution, which was first introduced in AlexNet [21] for distributing the model over two GPUs, has been well demonstrated its effectiveness in ResNeXt [40]. Depthwise separable convolution proposed in Xception [3] generalizes the ideas of separable convolutions in Inception series [34, 32]. Recently, MobileNet [12] utilizes the depthwise separable convolutions and gains state-of-the-art results among lightweight models. Our work generalizes group convolution and depthwise separable convolution in a novel form. Modern convolutional neural networks [30, 33, 34, 32, 9, 10] usually consist of repeated building blocks with the same structure. Among them, state-of-the-art networks such as Xception [3] and ResNeXt [40] introduce efficient depthwise separable convolutions or group convolutions into the building blocks to strike an excellent trade-off between representation capability and computational cost. However, we notice that both designs do not fully take the 1\times 1 convolutions (also called pointwise convolutions in  [12]) into account, which require considerable complexity. For example, in ResNeXt [40] only 3\times 3 layers are equipped with group convolutions. As a result, for each residual unit in ResNeXt the pointwise convolutions occupy 93.4% multiplication-adds (cardinality = 32 as suggested in  [40]). In tiny networks, expensive pointwise convolutions result in limited number of channels to meet the complexity constraint, which might significantly damage the accuracy.

Question: Which networks introduced efficient depthwise seperable convolution into the building blocks of a state-of-the-art network?
Although AlexNet introduced the idea of group convolutions, the Xception and ResNeXt generalized depthwise separable convolutions and achieved state-of-the-art results under large computationl budget (~1 GFLOPs).