Real-Time Cortical Simulation on Neuromorphic Hardware

TL;DR

This paper demonstrates the first true real-time large-scale cortical microcircuit simulation on neuromorphic hardware, achieving significant speed, energy efficiency, and robustness, surpassing traditional HPC and GPU-based methods.

Contribution

It introduces a heterogeneous parallelisation scheme on SpiNNaker hardware enabling real-time simulation of a complex cortical microcircuit, with verified accuracy and enhanced energy efficiency.

Findings

Achieved real-time simulation of 77k neurons and 0.3 billion synapses.

Reduced energy consumption by 10x compared to HPC systems.

Maintained robustness over multiple 12-hour simulations.

Abstract

Real-time simulation of a large-scale biologically representative spiking neural network is presented, through the use of a heterogeneous parallelisation scheme and SpiNNaker neuromorphic hardware. A published cortical microcircuit model is used as a benchmark test case, representing approx. 1 square mm of early sensory cortex, containing 77k neurons and 0.3 billion synapses. This is the first true real-time simulation of this model, with 10 s of biological simulation time executed in 10 s wall-clock time. This surpasses best published efforts on HPC neural simulators (3x slowdown) and GPUs running optimised SNN libraries (2x slowdown). Furthermore, the presented approach indicates that real-time processing can be maintained with increasing SNN size, breaking the communication barrier incurred by traditional computing machinery. Model results are compared to an established HPC simulator baseline to verify simulation correctness, comparing well across a range of statistical measures. Energy to solution, and energy per synaptic event are also reported, demonstrating that the relatively low-tech SpiNNaker processors achieve a 10x reduction in energy relative to modern HPC systems, and comparable energy consumption to modern GPUs. Finally, system robustness is demonstrated through multiple 12 h simulations of the cortical microcircuit, each simulating 12 h of biological time, and demonstrating the potential of neuromorphic hardware as a neuroscience research tool for studying complex spiking neural networks over extended time periods.