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In this paper, we propose a novel network design mechanism for efficientembedded computing. Inspired by the limited computing patterns, we propose tofix the number of channels in a group convolution, instead of the existingpractice that fixing the total group numbers. Our solution based network, namedVariable Group Convolutional Network (VarGNet), can be optimized easier onhardware 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 thepractical value of our VarGNet.

VarGNet: Variable Group Convolutional Neural Network for Efficient Embedded Computing