In many neuromorphic workflows, simulators play a vital role for important tasks such as training spiking neural networks (SNNs), running neuroscience simulations, and designing, implementing and testing neuromorphic algorithms. Currently available simulators are catered to either neuroscience workflows (such as NEST and Brian2) or deep learning workflows (such as BindsNET). While the neuroscience-based simulators are slow and not very scalable, the deep learning-based simulators do not support certain functionalities such as synaptic delay that are typical of neuromorphic workloads. In this paper, we address this gap in the literature and present SuperNeuro, which is a fast and scalable simulator for neuromorphic computing, capable of both homogeneous and heterogeneous simulations as well as GPU acceleration. We also present preliminary results comparing SuperNeuro to widely used neuromorphic simulators such as NEST, Brian2 and BindsNET in terms of computation times. We demonstrate that SuperNeuro can be approximately 10--300 times faster than some of the other simulators for small sparse networks. On large sparse and large dense networks, SuperNeuro can be approximately 2.2 and 3.4 times faster than the other simulators respectively.

翻译：在许多神经形态工作流中，模拟器在脉冲神经网络（SNN）训练、神经科学仿真运行以及神经形态算法的设计、实现和测试等关键任务中扮演着重要角色。当前可用的模拟器主要面向神经科学工作流（如NEST和Brian2）或深度学习工作流（如BindsNET）。然而，基于神经科学的模拟器运行缓慢且扩展性不足，而基于深度学习的模拟器则不支持神经形态计算中典型的突触延迟等功能。针对文献中的这一空白，本文提出SuperNeuro——一种快速且可扩展的神经形态计算模拟器，支持同质/异质仿真及GPU加速。我们还展示了初步实验结果，将SuperNeuro与广泛使用的神经形态模拟器（如NEST、Brian2和BindsNET）在计算时间方面进行对比。实验证明，对于小型稀疏网络，SuperNeuro的速度约为其他模拟器的10至300倍；对于大型稀疏网络和大型密集网络，SuperNeuro的速度分别约为其他模拟器的2.2倍和3.4倍。