  The field of view (FOV) of convolutional neural networks is highly related to
the accuracy of inference. Dilated convolutions are known as an effective
solution to the problems which require large FOVs. However, for general-purpose
hardware or dedicated hardware, it usually takes extra time to handle dilated
convolutions compared with standard convolutions. In this paper, we propose a
network module, Cascaded and Separable Structure of Dilated (CASSOD)
Convolution, and a special hardware system to handle the CASSOD networks
efficiently. A CASSOD-Net includes multiple cascaded $2 \times 2$ dilated
filters, which can be used to replace the traditional $3 \times 3$ dilated
filters without decreasing the accuracy of inference. Two example applications,
face detection and image segmentation, are tested with dilated convolutions and
the proposed CASSOD modules. The new network for face detection achieves higher
accuracy than the previous work with only 47% of filter weights in the dilated
convolution layers of the context module. Moreover, the proposed hardware
system can accelerate the computations of dilated convolutions, and it is 2.78
times faster than traditional hardware systems when the filter size is $3
\times 3$.
