In this paper, we propose a design methodology to partition and map the neurons and synapses of online learning SNN-based applications to neuromorphic architectures at {run-time}. Our design methodology operates in two steps -- step 1 is a layer-wise greedy approach to partition SNNs into clusters of neurons and synapses incorporating the constraints of the neuromorphic architecture, and step 2 is a hill-climbing optimization algorithm that minimizes the total spikes communicated between clusters, improving energy consumption on the shared interconnect of the architecture. We conduct experiments to evaluate the feasibility of our algorithm using synthetic and realistic SNN-based applications. We demonstrate that our algorithm reduces SNN mapping time by an average 780x compared to a state-of-the-art design-time based SNN partitioning approach with only 6.25\% lower solution quality.

中文翻译：

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尖峰神经网络到神经形态硬件的运行时映射

在本文中，我们提出了一种设计方法，用于在{运行时}将基于 SNN 的在线学习应用程序的神经元和突触分区和映射到神经形态架构。我们的设计方法分两步运行——第 1 步是一种分层贪婪方法，将 SNN 划分为包含神经形态架构约束的神经元和突触簇，第 2 步是爬山优化算法，可最大限度地减少总尖峰集群之间进行通信，降低了架构共享互连上的能耗。我们进行实验以使用基于合成和现实 SNN 的应用程序来评估我们算法的可行性。