  Extracting features from a huge amount of data for object recognition is a
challenging task. Convolution neural network can be used to meet the challenge,
but it often requires a large number of computation resources. In this paper, a
computation-efficient convolutional module, named SdcBlock, is proposed and
based on it, the convolution network SdcNet is introduced for object
recognition tasks. In the proposed module, optimized successive depthwise
convolutions supported by appropriate data management is applied in order to
generate vectors containing high density and more varieties of feature
information. The hyperparameters can be easily adjusted to suit varieties of
tasks under different computation restrictions without significantly
jeopardizing the performance. The experiments have shown that SdcNet achieved
an error rate of 5.60% in CIFAR-10 with only 55M Flops and also reduced further
the error rate to 5.24% using a moderate volume of 103M Flops. The expected
computation efficiency of the SdcNet has been confirmed.
