Effective and Efficient Computation with Multiple-timescale Spiking Recurrent Neural Networks
Schedule
Monday, August 31, 2020
14:10 UTC
Sander Bohte
Prof
Centrum Wiskunde & Informatica, Amsterdam
Host: SNUFA
Recording
Open recording
Event Information
Recording
Available
Host
SNUFA
Duration
70 minutes
Abstract
The emergence of brain-inspired neuromorphic computing as a paradigm for edge AI is motivating the search for high-performance and efficient spiking neural networks to run on this hardware. However, compared to classical neural networks in deep learning, current spiking neural networks lack competitive performance in compelling areas. Here, for sequential and streaming tasks, we demonstrate how spiking recurrent neural networks (SRNN) using adaptive spiking neurons are able to achieve state-of-the-art performance compared to other spiking neural networks and almost reach or exceed the performance of classical recurrent neural networks (RNNs) while exhibiting sparse activity. From this, we calculate a 100x energy improvement for our SRNNs over classical RNNs on the harder tasks. We find in particular that adapting the timescales of spiking neurons is crucial for achieving such performance, and we demonstrate the performance for SRNNs for different spiking neuron models.
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