Keyword spotting (KWS) provides a critical user interface for many mobile and edge applications, including phones, wearables, and cars. As KWS systems are typically 'always on', maximizing both accuracy and power efficiency are central to their utility. In this work we use hardware aware training (HAT) to build new KWS neural networks based on the Legendre Memory Unit (LMU) that achieve state-of-the-art (SotA) accuracy and low parameter counts. This allows the neural network to run efficiently on standard hardware (212$\mu$W). We also characterize the power requirements of custom designed accelerator hardware that achieves SotA power efficiency of 8.79$\mu$W, beating general purpose low power hardware (a microcontroller) by 24x and special purpose ASICs by 16x.

中文翻译：

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在通用和专用硬件上进行高效关键字识别的硬件意识培训

关键字定位 (KWS) 为许多移动和边缘应用程序（包括电话、可穿戴设备和汽车）提供了关键的用户界面。由于 KWS 系统通常“永远在线”，因此最大限度地提高准确性和电源效率是其效用的核心。在这项工作中，我们使用硬件感知训练 (HAT) 来构建基于勒让德记忆单元 (LMU) 的新 KWS 神经网络，以实现最先进的 (SotA) 精度和低参数计数。这允许神经网络在标准硬件（212$\mu$W）上高效运行。我们还描述了定制设计的加速器硬件的功率要求，其 SotA 功率效率为 8.79$\mu$W，比通用低功耗硬件（微控制器）高 24 倍，比专用 ASIC 高 16 倍。