# Closing the loop: High-speed robotics with accelerated neuromorphic hardware

## TL;DR

This paper introduces a high-speed robotic system using accelerated neuromorphic hardware for real-time control of electric motors.

## Contribution

A configurable real-time event interface is added to the BrainScaleS-2 system for high-speed robotics applications.

## Key findings

- The system achieves a 1,000-fold acceleration of emulated nerve cells for microsecond-scale control.
- A closed-loop setup demonstrates motor control using a spiking neural network trained with PyTorch.

## Abstract

The BrainScaleS-2 system is an established analog neuromorphic platform with versatile applications in the diverse fields of computational neuroscience and spike-based machine learning. In this work, we extend the system with a configurable realtime event interface that enables a tight coupling of its distinct analog network core to external sensors and actuators. The 1,000-fold acceleration of the emulated nerve cells allows us to target high-speed robotic applications that require precise timing on a microsecond scale. As a showcase, we present a closed-loop setup for commuting brushless DC motors: we utilize PyTorch to train a spiking neural network emulated on the analog substrate to control an electric motor from a sensory event stream. The presented system enables research in the area of event-driven controllers for high-speed robotics, including self-supervised and biologically inspired online learning for such applications.