Keyword spotting (KWS) is a crucial function enabling the interaction with
the many ubiquitous smart devices in our surroundings, either activating them
through wake-word or directly as a human-computer interface. For many
applications, KWS is the entry point for our interactions with the device and,
thus, an always-on workload. Many smart devices are mobile and their battery
lifetime is heavily impacted by continuously running services. KWS and similar
always-on services are thus the focus when optimizing the overall power
consumption. This work addresses KWS energy-efficiency on low-cost
microcontroller units (MCUs). We combine analog binary feature extraction with
binary neural networks. By replacing the digital preprocessing with the
proposed analog front-end, we show that the energy required for data
acquisition and preprocessing can be reduced by 29x, cutting its share from a
dominating 85% to a mere 16% of the overall energy consumption for our
reference KWS application. Experimental evaluations on the Speech Commands
Dataset show that the proposed system outperforms state-of-the-art accuracy and
energy efficiency, respectively, by 1% and 4.3x on a 10-class dataset while
providing a compelling accuracy-energy trade-off including a 2% accuracy drop
for a 71x energy reduction.

本文提出了一种在低成本微控制器单元上实现关键词识别的能量高效方法，其中包括使用模拟 - 二进制前端和二进制神经网络实现数字预处理的替换，将数据采集和预处理所需的能量减少了 29 倍，同时在 Speech Commands 数据集上提供 1% 的精度提高和 4.3 倍的能量效率提高，并提供 2% 的精度下降以实现 71 倍的能量削减。