With serial and parallel processors are introduced into Spiking Neural Networks (SNNs) execution, more and more researchers are dedicated to improving the performance of the computing paradigms by taking full advantage of strengths of the available processor. In this paper, we compare and integrate serial and parallel paradigms into one SNN compiling system. For a faster switching between them in the layer granularity, we train the classifier to prejudge a better paradigm before compiling instead of making decision afterwards, saving a great amount of compiling time and RAM space on host PC. The classifier Adaptive Boost with the highest accuracy (91.69 percent) among 12 classifiers is integrated into the switching system, which utilizes less memory and processors on the multi-core neuromorphic hardware backend SpiNNaker2 than two individual paradigms. To the best of our knowledge, it is the first fast switching compiling system for SNN simulation.

翻译：随着串行与并行处理器被引入脉冲神经网络（SNN）的执行过程，越来越多的研究者致力于充分利用现有处理器的优势以提升计算范式的性能。本文在同一个SNN编译系统中对比并整合了串行与并行范式。为实现层粒度上更快速的范式切换，我们训练了一个分类器在编译前预先判断更优范式，而非在编译后进行决策，从而在主机上节省了大量编译时间与内存空间。在12种分类器中准确率最高（91.69%）的自适应增强分类器被集成至该切换系统，其在多核神经形态硬件后端SpiNNaker2上占用的内存与处理器资源少于两种独立范式。据我们所知，这是首个面向SNN仿真的快速切换编译系统。