WaveSense: Efficient Temporal Convolutions with Spiking Neural Networks for Keyword Spotting

• URL: http://arxiv.org/abs/2111.01456v1
• Date: Tue, 2 Nov 2021 09:38:22 GMT
• Title: WaveSense: Efficient Temporal Convolutions with Spiking Neural Networks for Keyword Spotting
• Authors: Philipp Weidel, Sadique Sheik
• Abstract summary: We propose spiking neural dynamics as a natural alternative to dilated temporal convolutions. We extend this idea to WaveSense, a spiking neural network inspired by the WaveNet architecture.
• Score: 1.0152838128195467
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Ultra-low power local signal processing is a crucial aspect for edge applications on always-on devices. Neuromorphic processors emulating spiking neural networks show great computational power while fulfilling the limited power budget as needed in this domain. In this work we propose spiking neural dynamics as a natural alternative to dilated temporal convolutions. We extend this idea to WaveSense, a spiking neural network inspired by the WaveNet architecture. WaveSense uses simple neural dynamics, fixed time-constants and a simple feed-forward architecture and hence is particularly well suited for a neuromorphic implementation. We test the capabilities of this model on several datasets for keyword-spotting. The results show that the proposed network beats the state of the art of other spiking neural networks and reaches near state-of-the-art performance of artificial neural networks such as CNNs and LSTMs.

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