Versatile emulation of spiking neural networks on an accelerated neuromorphic substrate

Abstract

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.

References in corpus (10)

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Cited by in corpus (12)

• Event-Based Backpropagation can compute Exact Gradients for Spiking Neural Networks
• Bottom-up and top-down approaches for the design of neuromorphic processing systems: Tradeoffs and synergies between natural and artificial intelligence
• Extending BrainScaleS OS for BrainScaleS-2
• Demonstrating Analog Inference on the BrainScaleS-2 Mobile System
• Spiking neuromorphic chip learns entangled quantum states
• Fast and energy-efficient neuromorphic deep learning with first-spike times
• Learning through structure: towards deep neuromorphic knowledge graph embeddings
• An energy-based model for neuro-symbolic reasoning on knowledge graphs
• Evolving to learn: discovering interpretable plasticity rules for spiking networks
• Optimized spiking neurons classify images with high accuracy through temporal coding with two spikes
• Relational representation learning with spike trains
• Quantifying the Computational Capability of a Nanomagnetic Reservoir Computing Platform with Emergent Magnetization Dynamics