  Mobile and edge computing devices for always-on classification tasks require
energy-efficient neural network architectures. In this paper we present several
changes to neural architecture searches (NAS) that improve the chance of
success in practical situations. Our search simultaneously optimizes for
network accuracy, energy efficiency and memory usage. We benchmark the
performance of our search on real hardware, but since running thousands of
tests with real hardware is difficult we use a random forest model to roughly
predict the energy usage of a candidate network. We present a search strategy
that uses both Bayesian and regularized evolutionary search with particle
swarms, and employs early-stopping to reduce the computational burden. Our
search, evaluated on a sound-event classification dataset based upon AudioSet,
results in an order of magnitude less energy per inference and a much smaller
memory footprint than our baseline MobileNetV1/V2 implementations while
slightly improving task accuracy. We also demonstrate how combining a 2D
spectrogram with a convolution with many filters causes a computational
bottleneck for audio classification and that alternative approaches reduce the
computational burden but sacrifice task accuracy.
