Using OpenCL-based high-level synthesis, we create a number of spiking neural network (SNN) simulators for the Potjans-Diesmann cortical microcircuit for a high-end Field-Programmable Gate Array (FPGA). Our best simulators simulate the circuit 25\% faster than real-time, require less than 21 nJ per synaptic event, and are bottle-necked by the device's on-chip memory. Speed-wise they compare favorably to the state-of-the-art GPU-based simulators and their energy usage is lower than any other published result. This result is the first for simulating the circuit on a single hardware accelerator. We also extensively analyze the techniques and algorithms we implement our simulators with, many of which can be realized on other types of hardware. Thus, this article is of interest to any researcher or practitioner interested in efficient SNN simulation, whether they target FPGAs or not.

翻译：利用基于OpenCL的高层次综合方法，我们为高端现场可编程门阵列（FPGA）构建了多个针对Potjans-Diesmann皮层微电路的脉冲神经网络（SNN）仿真器。性能最优的仿真器运行速度比实时快25%，每个突触事件能耗低于21 nJ，其性能瓶颈在于器件的片上存储。在速度方面，它们优于当前最先进的基于GPU的仿真器，且能耗低于任何已发表的结果。这是首次在单一硬件加速器上实现该电路的仿真。我们还深入分析了仿真器所采用的技术与算法，其中许多可迁移至其他硬件类型。因此，本文对任何关注高效SNN仿真的研究人员或实践者（无论其是否以FPGA为目标）均具有参考价值。