We present a novel software feature for the BrainScaleS-2 accelerated neuromorphic platform that facilitates the partitioned emulation of large-scale spiking neural networks. This approach is well suited for deep spiking neural networks and allows for sequential model emulation on undersized neuromorphic resources if the largest recurrent subnetwork and the required neuron fan-in fit on the substrate. 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. 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.

翻译：我们为BrainScaleS-2加速神经形态平台提出了一种新颖的软件功能，该功能支持大规模脉冲神经网络的分区模拟。该方法特别适用于深度脉冲神经网络，只要最大递归子网络及所需神经元扇入能在基底上实现，即可在资源受限的神经形态硬件上实现顺序模型模拟。这种模拟和训练超出基底规模网络的能力，为规划中或扩展系统的精确性能评估提供了途径，最终推动大规模模型及神经形态计算架构的开发与理解。我们通过使用MNIST和EuroSAT数据集训练两个深度脉冲神经网络模型进行了验证，这些模型均突破了单芯片BrainScaleS-2系统的物理规模限制。