Using Intel's Loihi neuromorphic research chip and ABR's Nengo Deep Learning
toolkit, we analyze the inference speed, dynamic power consumption, and energy
cost per inference of a two-layer neural network keyword spotter trained to
recognize a single phrase. We perform comparative analyses of this keyword
spotter running on more conventional hardware devices including a CPU, a GPU,
Nvidia's Jetson TX1, and the Movidius Neural Compute Stick. Our results
indicate that for this inference application, Loihi outperforms all of these
alternatives on an energy cost per inference basis while maintaining equivalent
inference accuracy. Furthermore, an analysis of tradeoffs between network size,
inference speed, and energy cost indicates that Loihi's comparative advantage
over other low-power computing devices improves for larger networks.

使用英特尔的 Loihi 神经形态研究芯片和 ABR 的 Nengo 深度学习工具包，对训练识别单个短语的两层神经网络关键词检测器进行推理速度、动态功耗和每次推理的能量成本的分析。与运行在其他硬件设备上的关键词检测器进行比较分析，包括 CPU、GPU、Nvidia 的 Jetson TX1 和 Movidius 神经计算棒。我们的结论表明，在这种推理应用中，Loihi 比所有这些替代品在每次推理的能量成本方面表现更好，同时保持相同的推理精度。此外，对网络大小、推理速度和能量成本之间的权衡分析表明，Loihi 相对其他低功耗计算设备的竞争优势在更大的网络中得到了提高。