Leveraging Sparsity with Spiking Recurrent Neural Networks for Energy-Efficient Keyword Spotting

Abstract

Bio-inspired Spiking Neural Networks (SNNs) are promising candidates to replace standard Artificial Neural Networks (ANNs) for energy-efficient keyword spotting (KWS) systems. In this work, we compare the trade-off between accuracy and energy-efficiency of a gated recurrent SNN (Spik-GRU) with a standard Gated Recurrent Unit (GRU) on the Google Speech Command Dataset (GSCD) v2. We show that, by taking advantage of the sparse spiking activity of the SNN, both accuracy and energy-efficiency can be increased. Lever-aging data sparsity by using spiking inputs, such as those produced by spiking audio feature extractors or dynamic sensors, can further improve energy-efficiency. We demonstrate state-of-the-art results for SNNs on GSCD v2 with up to 95.9% accuracy. Moreover, SpikGRU can achieve similar accuracy than GRU while reducing the number of operations by up to 82%.