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.

中文翻译：

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NeuroXplorer 1.0：使用尖刺神经网络进行建筑探索的可扩展框架

最近，行业和学术界都提出了许多不同的神经形态架构来执行使用Spiking Neural Network（SNN）设计的应用程序。因此，对可扩展的仿真框架的需求日益增长，该框架可以使用SNN进行架构探索，包括当今硬件的基于平台的设计以及未来的硬件-软件协同设计和设计技术协同优化。我们介绍NeuroXplorer，这是一个快速且可扩展的框架，该框架基于用于建模神经形态架构的通用模板，该形态可以注入给定硬件和/或技术的特定细节。NeuroXplorer可以执行低周期精确度的体系结构仿真，也可以执行数据流抽象的高级分析。NeuroXplorer' 的优化引擎可以包含面向硬件的指标，例如能量，吞吐量和延迟，以及面向SNN的指标，例如尖峰间隔失真和尖峰混乱，它们直接影响SNN的性能。我们通过案例研究和许多最新的机器学习模型，展示了NeuroXplorer的架构探索能力。