Neuromorphic VLSI systems take inspiration from biology to enable efficient emulation of large-scale spiking neural networks and to explore new computational paradigms. To establish large neuromorphic systems, a sophisticated routing infrastructure is needed to communicate spikes between chips and to/from the host computer. For the BrainScaleS wafer-scale neuromorphic system considered in this work, especially the stimulation with input spikes and the recording of spikes is demanding, requiring high bandwidth and temporal resolution due to the accelerated emulation of neural dynamics 10.000 faster than biological real time. Here, we present a systematic characterization of the BrainScaleS off-wafer communication infrastructure implemented around Kintex7 FPGAs. The communication flow is characterized in terms of throughput, transmission delay, jitter and pulse loss. Further, we analyze the effect of the communication distortions (like pulse loss and jitter) on a neural benchmark model with highly varying spike activity. The presented methods and techniques for communication evaluation are general applicable and provide useful insights for the mapping of network models to the hardware such as the distribution of input spikes across communication channels.

翻译：神经形态超大规模集成电路系统借鉴生物学原理，能够高效模拟大规模脉冲神经网络，并探索新的计算范式。为构建大型神经形态系统，需要复杂的路由基础设施来实现芯片间及芯片与主机计算机的脉冲通信。针对本文研究的BrainScaleS晶圆级神经形态系统，其输入脉冲刺激与脉冲记录尤为关键：由于神经动力学仿真加速至生物实时的一万倍，这对通信带宽和时间分辨率提出了极高要求。本文对基于Kintex7 FPGA实现的BrainScaleS晶圆外通信基础设施进行了系统性特性分析，从吞吐量、传输延迟、抖动和脉冲丢失等维度表征通信流程。进一步，我们分析了通信畸变（如脉冲丢失与抖动）对具有高度可变脉冲活动的神经基准模型的影响。所提出的通信评估方法和技术具有普适性，可为网络模型到硬件的映射（如跨通信通道的输入脉冲分配）提供有效指导。