  Spiking Neural Networks (SNNs) provide an efficient computational mechanism
for temporal signal processing, especially when coupled with low-power SNN
inference ASICs. SNNs have been historically difficult to configure, lacking a
general method for finding solutions for arbitrary tasks. In recent years,
gradient-descent optimization methods have been applied to SNNs with increasing
ease. SNNs and SNN inference processors therefore offer a good platform for
commercial low-power signal processing in energy constrained environments
without cloud dependencies. However, to date these methods have not been
accessible to ML engineers in industry, requiring graduate-level training to
successfully configure a single SNN application. Here we demonstrate a
convenient high-level pipeline to design, train and deploy arbitrary temporal
signal processing applications to sub-mW SNN inference hardware. We apply a new
straightforward SNN architecture designed for temporal signal processing, using
a pyramid of synaptic time constants to extract signal features at a range of
temporal scales. We demonstrate this architecture on an ambient audio
classification task, deployed to the Xylo SNN inference processor in streaming
mode. Our application achieves high accuracy (98%) and low latency (100ms) at
low power (<100$\mu$W inference power). Our approach makes training and
deploying SNN applications available to ML engineers with general NN
backgrounds, without requiring specific prior experience with spiking NNs. We
intend for our approach to make Neuromorphic hardware and SNNs an attractive
choice for commercial low-power and edge signal processing applications.
