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Analog feature extraction is a power-efficient and re-emerging signalprocessing paradigm for implementing the front-end feature extractor in ondevice keyword-spotting systems. Despite its power efficiency and re-emergence,there is little consensus on what values the architectural parameters of itscritical block, the analog filterbank, should be set to, even though theystrongly influence power consumption. Towards building consensus andapproaching fundamental power consumption limits, we find via simulation thatthrough careful selection of its architectural parameters, the power of atypical state-of-the-art analog filterbank could be reduced by 33.6x, whilesacrificing only 1.8% in downstream 10-word keyword spotting accuracy through aback-end neural network.

How Tiny Can Analog Filterbank Features Be Made for Ultra-low-power On-device Keyword Spotting?