Convolutional Neural Networks (CNNs) have become the state of the art for many computer vision tasks. Their highly parallel computation graph structure offers many possibilities for optimization of hardware. While the precision and the accuracy of state-of-the-art CNN are remarkable, the deriving computational complexity and significant amount of data movement pose a challenge in terms of energy efficiency. Focussing on moving the computational complexity off-chip and reducing the off-chip↔on-chip communication shows successful results in terms of energy efficiency and throughput. Due to the deterministic nature of CNN execution, the communication does not necessarily require complex routing algorithms typically supported by Network-on-Chips (NoCs).