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

NeuroXplorer 1.0: An Extensible Framework for Architectural Exploration with Spiking Neural Networks