Towards Large-scale Network Emulation on Analog Neuromorphic Hardware
CoRR(2024)
摘要
We present a novel software feature for the BrainScaleS-2 accelerated
neuromorphic platform that facilitates the emulation of partitioned large-scale
spiking neural networks. This approach is well suited for many deep spiking
neural networks, where the constraint of the largest recurrent subnetwork
fitting on the substrate or the limited fan-in of neurons is often not a
limitation in practice. We demonstrate the training of two deep spiking neural
network models, using the MNIST and EuroSAT datasets, that exceed the physical
size constraints of a single-chip BrainScaleS-2 system. The ability to emulate
and train networks larger than the substrate provides a pathway for accurate
performance evaluation in planned or scaled systems, ultimately advancing the
development and understanding of large-scale models and neuromorphic computing
architectures.
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