  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.
