Neural Architecture Search for Energy Efficient Always-on Audio Models

Mobile and edge computing devices for always-on audio classification require energy-efficient neural network architectures. We present a neural architecture search (NAS) that optimizes accuracy, energy efficiency and memory usage. The search is run on Vizier, a black-box optimization service. 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. The search returns architectures for a sound-event classification dataset based upon AudioSet with similar accuracy to MobileNetV1/V2 implementations but with an order of magnitude less energy per inference and a much smaller memory footprint.