  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.
