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

Hardware Aware Training for Efficient Keyword Spotting on General Purpose and Specialized Hardware