학술논문

How Tiny Can Analog Filterbank Features Be Made for Ultra-low-power On-device Keyword Spotting?
Document Type
Working Paper
Source
Subject
Electrical Engineering and Systems Science - Audio and Speech Processing
Computer Science - Sound
Language
Abstract
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.
Comment: Accepted as a full paper by the TinyML Research Symposium 2023