DYNAP-CNN is a scalable, fully-configurable digital event-driven neuromorphic processor with 1M ReLU spiking neurons per chip for implementing Spiking Convolutional Neural Networks (SCNN). This technology is ideal for always-on, ultra-low power and ultra-low latency event-driven sensory processing applications. With a dedicated interface for dynamic-vision-sensors, it allows direct input of event streams from most of the advanced dynamic-vision-senors in the world, enabling seamless integration and rapid prototyping of models. DYNAP-CNN is fully configurable and supports various types of CNN layers (like ReLU, Cropping, Padding and Pooling) and network models (like LeNet, ResNet and Inception). It provides complete control of your models with extensive programmablility of all of its parameters. In addition, DYNAP-CNN is scalable, enabling implementation of deep neural networks with unlimited number of layers over multiple interconnected DYNAP-CNNs.