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.

中文翻译：

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闭环：具有加速神经形态硬件的高速机器人

BrainScaleS-2 系统是一个成熟的模拟神经形态平台，在计算神经科学和基于尖峰的机器学习的各个领域具有多种应用。在这项工作中，我们通过可配置的实时事件接口扩展了系统，该接口能够将其独特的模拟网络核心与外部传感器和执行器紧密耦合。模拟神经细胞的 1,000 倍加速使我们能够瞄准需要微秒级精确计时的高速机器人应用。作为展示，我们展示了用于通勤无刷直流电机的闭环设置：我们利用 PyTorch 训练在模拟基板上模拟的尖峰神经网络，以通过感官事件流控制电机。所提出的系统支持高速机器人事件驱动控制器领域的研究，包括针对此类应用的自我监督和生物启发在线学习。