  Analog feature extraction is a power-efficient and re-emerging signal
processing paradigm for implementing the front-end feature extractor in on
device keyword-spotting systems. Despite its power efficiency and re-emergence,
there is little consensus on what values the architectural parameters of its
critical block, the analog filterbank, should be set to, even though they
strongly influence power consumption. Towards building consensus and
approaching fundamental power consumption limits, we find via simulation that
through careful selection of its architectural parameters, the power of a
typical state-of-the-art analog filterbank could be reduced by 33.6x, while
sacrificing only 1.8% in downstream 10-word keyword spotting accuracy through a
back-end neural network.
