Versatile emulation of spiking neural networks on an accelerated neuromorphic substrate
This work addresses the problem of computational efficiency in neuromorphic computing for researchers, though it appears incremental as it builds on existing neuromorphic hardware with new experimental validation.
The authors tackled the challenge of efficiently emulating spiking neural networks by presenting experimental results on the BrainScaleS-2 neuromorphic architecture, achieving a high acceleration factor of 1000 compared to biological dynamics and demonstrating flexibility through five distinct experiments.
We present first experimental results on the novel BrainScaleS-2 neuromorphic architecture based on an analog neuro-synaptic core and augmented by embedded microprocessors for complex plasticity and experiment control. The high acceleration factor of 1000 compared to biological dynamics enables the execution of computationally expensive tasks, by allowing the fast emulation of long-duration experiments or rapid iteration over many consecutive trials. The flexibility of our architecture is demonstrated in a suite of five distinct experiments, which emphasize different aspects of the BrainScaleS-2 system.