We implemented two neural network based benchmark tasks on a prototype chip of the second-generation SpiNNaker (SpiNNaker 2) neuromorphic system: keyword spotting and adaptive robotic control. Keyword spotting is commonly used in smart speakers to listen for wake words, and adaptive control is used in robotic applications to adapt to unknown dynamics in an online fashion. We highlight the benefit of a multiply accumulate (MAC) array in the SpiNNaker 2 prototype which is ordinarily used in rate-based machine learning networks when employed in a neuromorphic, spiking context. In addition, the same benchmark tasks have been implemented on the Loihi neuromorphic chip, giving a side-by-side comparison regarding power consumption and computation time. While Loihi shows better efficiency when less complicated vector-matrix multiplication is involved, with the MAC array, the SpiNNaker 2 prototype shows better efficiency when high dimensional vector-matrix multiplication is involved.

中文翻译：

---

使用 SpiNNaker 2 原型的低功耗低延迟关键字定位和自适应控制以及与 Loihi 的比较

我们在第二代 SpiNNaker (SpiNNaker 2) 神经形态系统的原型芯片上实现了两个基于神经网络的基准测试任务：关键字识别和自适应机器人控制。关键字识别通常用于智能扬声器以收听唤醒词，而自适应控制用于机器人应用程序以在线方式适应未知动态。我们强调了 SpiNNaker 2 原型中乘法累加 (MAC) 数组的好处，当在神经形态、尖峰环境中使用时，它通常用于基于速率的机器学习网络。此外，在 Loihi 神经形态芯片上实现了相同的基准测试任务，对功耗和计算时间进行了并排比较。虽然 Loihi 在涉及不太复杂的向量矩阵乘法时表现出更好的效率，