  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.
