  For many years, designs for "Neuromorphic" or brain-like processors have been
motivated by achieving extreme energy efficiency, compared with von-Neumann and
tensor processor devices. As part of their design language, Neuromorphic
processors take advantage of weight, parameter, state and activity sparsity. In
the extreme case, neural networks based on these principles mimic the sparse
activity oof biological nervous systems, in ``Spiking Neural Networks'' (SNNs).
Few benchmarks are available for Neuromorphic processors, that have been
implemented for a range of Neuromorphic and non-Neuromorphic platforms, which
can therefore demonstrate the energy benefits of Neuromorphic processor
designs. Here we describes the implementation of a spoken audio
keyword-spotting (KWS) benchmark "Aloha" on the Xylo Audio 2 (SYNS61210)
Neuromorphic processor device. We obtained high deployed quantized task
accuracy, (95%), exceeding the benchmark task accuracy. We measured real
continuous power of the deployed application on Xylo. We obtained best-in-class
dynamic inference power ($291\mu$W) and best-in-class inference efficiency
($6.6\mu$J / Inf). Xylo sets a new minimum power for the Aloha KWS benchmark,
and highlights the extreme energy efficiency achievable with Neuromorphic
processor designs. Our results show that Neuromorphic designs are well-suited
for real-time near- and in-sensor processing on edge devices.
