Parallelization of Neural Processing on Neuromorphic Hardware

Learning and development in real brains typically happens over long timescales, making long-term exploration of these features a significant research challenge. One way to address this problem is to use computational models to explore the brain, with Spiking Neural Networks a popular choice to capture neuron and synapse dynamics. However, researchers require simulation tools and platforms to execute simulations in real- or sub-realtime, to enable exploration of features such as long-term learning and neural pathologies over meaningful periods. This article presents novel multicore processing strategies on the SpiNNaker Neuromorphic hardware, addressing parallelization of Spiking Neural Network operations through allocation of dedicated computational units to specific tasks (such as neural and synaptic processing) to optimize performance. The work advances previous real-time simulations of a cortical microcircuit model, parameterizing load balancing between computational units in order to explore trade-offs between computational complexity and speed, to provide the best fit for a given application. By exploiting the flexibility of the SpiNNaker Neuromorphic platform, up to 9x throughput of neural operations is demonstrated when running biologically representative Spiking Neural Networks.

Automated summary

This article presents novel multicore processing strategies on the SpiNNaker Neuromorphic hardware, which optimize the efficiency of Spiking Neural Network operations. By parameterizing load balancing between computational units, researchers are able to explore long-term learning and neural pathologies in real- or sub-realtime.