Sorry, you need to enable JavaScript to visit this website.

Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detection

Primary tabs

Citation Author(s):
Flavio Martinelli, Giorgia Dellaferrera, Pablo Mainar, Milos Cernak
Submitted by:
Flavio Martinelli
Last updated:
27 May 2020 - 8:49am
Document Type:
Presentation Slides
Document Year:
2020
Event:
Presenters Name:
Flavio Martinelli
Paper Code:
SS-L5.5

Abstract 

Abstract: 

Recent advances in Voice Activity Detection (VAD) are driven by artificial and Recurrent Neural Networks (RNNs), however, using a VAD system in battery-operated devices requires further power efficiency. This can be achieved by neuromorphic hardware, which enables Spiking Neural Networks (SNNs) to perform inference at very low energy consumption. Spiking networks are characterized by their ability to process information efficiently, in a sparse cascade of binary events in time called spikes. However, a big performance gap separates artificial from spiking networks, mostly due to a lack of powerful SNN training algorithms. To overcome this problem we exploit an SNN model that can be recast into a recurrent network and trained with known deep learning techniques. We describe a training procedure that achieves low spiking activity and apply pruning algorithms to remove up to 85% of the network connections with no performance loss. The model competes with state-of-the-art performance at a fraction of the power consumption comparing to other methods.

up
0 users have voted:

Dataset Files

icassp2020snn_slides.pdf

(176)