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.
翻译:我们在第二代SpinNNAker(SpinN纳ker 2)神经形态系统原型芯片上执行了基于两个神经网络的基准任务:关键字定位和适应性机器人控制。关键字定位通常用于智能演讲人听醒词,适应性控制用于机器人应用,以在线方式适应未知的动态。我们强调SpinNNAker 2原型中基于速率的机器学习网络在神经形态、弹跳背景下使用时通常使用的倍增累积(MAC)阵列的好处。此外,在Loihi神经形态芯片上也执行了同样的基准任务,对电耗和计算时间进行了逐边比较。 Loihi在涉及较不复杂的矢量矩阵倍增时,在涉及MAC阵列时,SpinNNaker 2原型在涉及高维矢量矩阵的倍增时显示出更高的效率。