  Recently, both industry and academia have proposed many different
neuromorphic architectures to execute applications that are designed with
Spiking Neural Network (SNN). Consequently, there is a growing need for an
extensible simulation framework that can perform architectural explorations
with SNNs, including both platform-based design of today's hardware, and
hardware-software co-design and design-technology co-optimization of the
future. We present NeuroXplorer, a fast and extensible framework that is based
on a generalized template for modeling a neuromorphic architecture that can be
infused with the specific details of a given hardware and/or technology.
NeuroXplorer can perform both low-level cycle-accurate architectural
simulations and high-level analysis with data-flow abstractions. NeuroXplorer's
optimization engine can incorporate hardware-oriented metrics such as energy,
throughput, and latency, as well as SNN-oriented metrics such as inter-spike
interval distortion and spike disorder, which directly impact SNN performance.
We demonstrate the architectural exploration capabilities of NeuroXplorer
through case studies with many state-of-the-art machine learning models.
