Home /Research /An Interface Platform for Robotic Neuromorphic Systems
LEARNING

An Interface Platform for Robotic Neuromorphic Systems

Nicola Russo, Haochun Huang, Eugenio Donati, Thomas Bruun Madsen, Konstantin Nikolić

Year
2023
Citations
11
Access
Open access

Abstract

Neuromorphic computing is promising to become a future standard in low-power AI applications. The integration between new neuromorphic hardware and traditional microcontrollers is an open challenge. In this paper, we present an interface board and a communication protocol that allows communication between different devices, using a microcontroller unit (Arduino Due) in the middle. Our compact printed circuit board (PCB) links different devices as a whole system and provides a power supply for the entire system using batteries as the power supply. Concretely, we have connected a Dynamic Vision Sensor (DVS128), SpiNNaker board and a servo motor, creating a platform for a neuromorphic robotic system controlled by a Spiking Neural Network, which is demonstrated on the task of intercepting incoming objects. The data rate of the implemented interface board is 24.64 k 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.

Keywords

Neuromorphic engineeringMicrocontrollerComputer scienceArduinoEmbedded systemInterface (matter)Computer hardwareProtocol (science)Artificial neural networkArtificial intelligence

Related papers

Browse all LEARNING papers