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.

翻译：用于音频分类的移动和边缘计算设备需要节能神经网络结构。 我们展示了一种能优化准确性、能效和记忆用量的神经结构搜索(NAS ) 。 搜索是在黑箱优化服务Vizier 上运行的。 我们展示了一种搜索策略,使用巴伊西亚人和常规进化搜索,使用粒子群,并使用早期停止来减少计算负担。 搜索返回了基于音频Set的音频活动分类数据集的搜索结构,该数据集的精确性与移动网络V1/V2的安装类似,但数量级小得多,能量透视率小得多,记忆足迹也小得多。