Neuromorphic Computing (NC) and Spiking Neural Networks (SNNs) in particular are often viewed as the next generation of Neural Networks (NNs). NC is a novel bio-inspired paradigm for energy efficient neural computation, often relying on SNNs in which neurons communicate via spikes in a sparse, event-based manner. This communication via spikes can be exploited by neuromorphic hardware implementations very effectively and results in a drastic reductions of power consumption and latency in contrast to regular GPU-based NNs. In recent years, neuromorphic hardware has become more accessible, and the support of learning frameworks has improved. However, available hardware is partially still experimental, and it is not transparent what these solutions are effectively capable of, how they integrate into real-world robotics applications, and how they realistically benefit energy efficiency and latency. In this work, we provide the robotics research community with an overview of what is possible with SNNs on neuromorphic hardware focusing on real-time processing. We introduce a benchmark of three popular neuromorphic hardware devices for the task of event-based object detection. Moreover, we show that an SNN on a neuromorphic hardware is able to run in a challenging table tennis robot setup in real-time.

翻译：神经形态计算（Neuromorphic Computing, NC），尤其是脉冲神经网络（Spiking Neural Networks, SNNs），常被视为下一代神经网络（Neural Networks, NNs）。NC是一种新颖的、受生物启发的、高能效的神经计算范式，通常依赖于SNNs，其中神经元通过脉冲以稀疏的、基于事件的方式进行通信。这种基于脉冲的通信方式可以被神经形态硬件实现高效利用，与常规基于GPU的神经网络相比，能显著降低功耗和延迟。近年来，神经形态硬件变得更加易于获取，学习框架的支持也有所改善。然而，现有硬件部分仍处于实验阶段，这些解决方案的实际能力、如何集成到现实世界的机器人应用中，以及它们对能效和延迟的实际益处，尚不明确。在本工作中，我们为机器人研究界提供了一个关于在神经形态硬件上使用SNNs进行实时处理可能性的概述。我们为基于事件的目标检测任务，引入了对三种流行神经形态硬件设备的基准测试。此外，我们展示了在神经形态硬件上运行的SNN能够在具有挑战性的乒乓球机器人设置中实时运行。