NeuroXplorer 1.0: An Extensible Framework for Architectural Exploration with Spiking Neural Networks
NeuroXplorer 1.0: An Extensible Framework for Architectural Exploration with Spiking Neural Networks
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DOI：
10.1145/3477145.3477156
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发表时间：
2021-05
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通讯作者：
Adarsha Balaji;Shihao Song;Twisha Titirsha;Anup Das;J. Krichmar;N. Dutt;J. Shackleford;Nagarajan Kandasamy;F. Catthoor
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作者：
Adarsha Balaji;Shihao Song;Twisha Titirsha;Anup Das;J. Krichmar;N. Dutt;J. Shackleford;Nagarajan Kandasamy;F. Catthoor
最近，工业界和学术界提出了许多不同的神经形态架构来执行使用尖峰神经网络（SNN）设计的应用程序。因此，越来越需要一个可扩展的仿真框架，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。我们提出 NeuroXplorer，一个快速且可扩展的框架，基于用于建模神经形态架构的通用模板，该架构可以注入给定硬件和/或技术的具体细节。 NeuroXplorer 可以执行低级周期精确架构模拟和使用数据流抽象的高级分析。 NeuroXplorer 的优化引擎可以结合面向硬件的指标（例如能量、吞吐量和延迟）以及面向 SNN 的指标（例如尖峰间间隔失真和尖峰无序），这些指标直接影响 SNN 性能。我们通过许多最先进的机器学习模型的案例研究展示了 NeuroXplorer 的架构探索能力。
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.