Neuromorphic computing is going to be the new standard in low power AI applications. The integration between new neuromorphic hardware and traditional microcontroller is an open challenge. Here we present an interface board and a communication protocol that allows the communication between different devices using a microcontroller unit (Arduino Due) in the middle. Our compact printed circuit board (PCB) board links different devices as a whole system and provides power supply for the entire system using batteries as power source. Specifically, we have connected a Dynamic Vision Sensor (DVS128), SpiNNaker board and a servo motor, creating a platform for intercepting incoming objects. The data rate of the implemented interface board is 24.64k symbols/s and the latency for generating commands is about 11ms. The complete system is run only by batteries making it very suitable for robotic applications.

In recent years, artificial neural networks (ANNs) have completely changed the field of machine learning, especially computer vision. The Spike Neural Network (SNN) is biologically more realistic than ANN. It is more hardware-friendly and energy-efficient, and suitable for running on portable devices with weak computing performance. In this paper we aim to classify several Chinese character images based on SNN. The input image is preprocessed by traditional methods (OpenCV) and then it is input into the trained spike neural network to classify the characters. Different hyperparameters configurations are tested reaching an optimal configuration and a classification accuracy rate of 93%.