Effective and Efficient Computation with Multiple-timescale Spiking Recurrent Neural Networks

Understand

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 a novel type of adaptive spiking recurrent neural network (SRNN) is 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.