  In this paper, we propose a novel network design mechanism for efficient
embedded computing. Inspired by the limited computing patterns, we propose to
fix the number of channels in a group convolution, instead of the existing
practice that fixing the total group numbers. Our solution based network, named
Variable Group Convolutional Network (VarGNet), can be optimized easier on
hardware side, due to the more unified computing schemes among the layers.
Extensive experiments on various vision tasks, including classification,
detection, pixel-wise parsing and face recognition, have demonstrated the
practical value of our VarGNet.
