Abstract
We introduce an extremely computation-efficient CNN
architecture named ShuffleNet, which is designed specially
for mobile devices with very limited computing power (e.g.,
10-150 MFLOPs). The new architecture utilizes two new
operations, pointwise group convolution and channel shuf-
fle, to greatly reduce computation cost while maintaining
accuracy. Experiments on ImageNet classification and MS
COCO object detection demonstrate the superior perfor-
mance of ShuffleNet over other structures, e.g. lower top-1
error (absolute 7.8%) than recent MobileNet [12] on Ima-
geNet classification task, under the computation budget of
40 MFLOPs. On an ARM-based mobile device, ShuffleNet
achieves ∼13× actual speedup over AlexNet while main-
taining comparable accuracy.
