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

Adarsha Balaji and Shihao Song and Twisha Titirsha and Anup Das and Jeffrey Krichmar and Nikil Dutt and James Shackleford and Nagarajan Kandasamy and Francky Catthoor
<span title="2021-05-04">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
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
more &raquo; ... k 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="">arXiv:2105.01795v1</a> <a target="_blank" rel="external noopener" href="">fatcat:yztiegjepvho5ecztv2akaj4vy</a> </span>
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