Dilated convolutions have become popular in recent Deep Neural Networks (DNNs). However, they introduce additional zeros within the weights. To efficiently compute these layers within image processing DNNs, we implemented a 10 TOPS neural network SoC in 22 nm that supports dilated convolutions with reduced energy consumption. By exploiting this advantage, the energy can be reduced by a factor of 4.46 even for DNNs such as DeepLabV3+ with a majority of standard convolutional layers.