Scaling Properties of Spiking Neural Networks on the Digital Neuromorphic Hardware SpiNNaker : An analysis of how increasing the number of neurons and the number of connections between them affects the performance of spiking neural networks on the SpiNNak

Abstract: SpiNNaker is a neuromorphic hardware devised to simulate SNNs effectively.This paper examines how the performance of an SNN simulated on SpiNNaker is affected by its ratio of number of neurons to number of connections between neurons, as the SNN grows in size. The scope of this paper is limited to the SpiNNaker system SpiNN-3. The performance was measured by running Vogels-Abbott benchmark networks, varying the neuron count and connection probability, and noting which configurations the hardware was able to handle. In total, 3; 498 different configurations were examined. The discussion suggests an improvement to the method that is not dependent upon an underlying assumption to be true. Regardless of whether this assumption is true or not, it is concluded there is a non-proportional relationship between the number of neurons and the connection probability. As the connection probability decreases, the number of neurons that can be simulated increases at a faster rate. Secondly, it is found that SpiNN-3 can simulate up to 12; 000 neurons without issues at a relatively high connection probability compared to the sparsely connected human brain. Thus it is concluded that SpiNN-3 lives up to its promise of being able to simulate ~10; 000 neurons. The scaling properties of SNNs on SpiNNaker are thereby determined to be promising. SpiNNaker2 is the planned sequel to the SpiNNaker version that has been investigated. The results of this study suggest, and most importantly, do not contradict, the possibility of a machine such as SpiNNaker2. It is the authors’ perception that SpiNNaker2 is a good candidate for leading the way into a sustainable future with brain-sized SNN-simulations. Several suggestions for future research are made.