  Nearly all previous work on small-footprint keyword spotting with neural
networks quantify model footprint in terms of the number of parameters and
multiply operations for a feedforward inference pass. These values are,
however, proxy measures since empirical performance in actual deployments is
determined by many factors. In this paper, we study the power consumption of a
family of convolutional neural networks for keyword spotting on a Raspberry Pi.
We find that both proxies are good predictors of energy usage, although the
number of multiplies is more predictive than the number of model parameters. We
also confirm that models with the highest accuracies are, unsurprisingly, the
most power hungry.
