Ensembles of Spiking Neural Networks
This work enables low-power edge applications, such as neuromorphic hardware, by creating efficient SNN systems with reduced memory footprints.
The paper tackled the challenge of improving spiking neural network (SNN) performance to compete with traditional deep neural networks by constructing ensembles, achieving state-of-the-art accuracies of 98.71%, 100.0%, and 99.09% on MNIST, NMNIST, and DVS Gesture datasets with fewer parameters.
This paper demonstrates how to construct ensembles of spiking neural networks producing state-of-the-art results, achieving classification accuracies of 98.71%, 100.0%, and 99.09%, on the MNIST, NMNIST and DVS Gesture datasets respectively. Furthermore, this performance is achieved using simplified individual models, with ensembles containing less than 50% of the parameters of published reference models. We provide comprehensive exploration on the effect of spike train interpretation methods, and derive the theoretical methodology for combining model predictions such that performance improvements are guaranteed for spiking ensembles. For this, we formalize spiking neural networks as GLM predictors, identifying a suitable representation for their target domain. Further, we show how the diversity of our spiking ensembles can be measured using the Ambiguity Decomposition. The work demonstrates how ensembling can overcome the challenges of producing individual SNN models which can compete with traditional deep neural networks, and creates systems with fewer trainable parameters and smaller memory footprints, opening the door to low-power edge applications, e.g. implemented on neuromorphic hardware.