Small-footprint Spiking Neural Networks for Power-efficient Keyword Spotting

Identifying specific keywords in speech is a common task present in current mobile devices. In this context, Keyword spotting (KWS) is the triggering cue for the device to start “listening in” on the uttered command. Since this cue can originate at any moment, KWS must be continuously performed. Small-footprint KWS is typically designed to directly detect the entire keyword (as opposed to sub-word components, such as phonemes), with current low-power KWS solutions relying heavily on artificial neural networks (ANNs). As a power-efficient alternative to ANNs, biologically-inspired spiking neural networks (SNNs) have achieved similar results in terms of accuracy, with the benefit of simpler and, typically, sparser computation. SNNs can be designed to operate in rate- and time-based modes, each with its own particularities. In this work, we propose a small-footprint KWS solution on six common words from the TIMIT speech dataset using fully connected, feedforward neural networks, and compare the frame error rate performance and computational cost of ANN-versus-SNN alternatives. For the cost comparison, we designed a figure of merit which takes into account the types of operations present in digital implementations of the different types of networks. Lastly, we discuss the promising results for power-efficient KWS using SNNs, particularly in the time-based domain, which shows equivalent accuracy performance yet with an 84% reduction in cost in relation to its ANN equivalent.