论文标题
使用大三角帆2原型和Loihi比较
Low-Power Low-Latency Keyword Spotting and Adaptive Control with a SpiNNaker 2 Prototype and Comparison with Loihi
论文作者
论文摘要
我们在第二代大三角器(Spinnaker 2)神经形态系统的原型芯片上实施了两个基于神经网络的基准任务:关键字发现和自适应机器人控制。关键字斑点通常用于智能扬声器中以聆听唤醒单词,并且在机器人应用中使用自适应控制,以适应在线方式中的未知动态。我们强调了在Spinnaker 2原型中的多重积累(Mac)阵列的好处,该原型通常用于基于费率的机器学习网络中,当时在神经形态的尖峰环境中。此外,在Loihi神经形态芯片上已经实施了相同的基准任务,从而对功耗和计算时间进行了并排比较。虽然涉及较不复杂的矢量矩阵乘法时,Loihi显示出更好的效率,但使用MAC阵列,Spinnaker 2原型在涉及高维矢量 - 矩阵乘法时显示出更好的效率。
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.