超越边割：面向片上网络（NoC）网格架构的脉冲神经网络（SNN）的活动加权多播超图映射
Beyond Edge Cuts: Activity-Weighted Multicast Hypergraph Mapping for Spiking Neural Networks on Mesh NoCs
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中文总结 AI 辅助
本文提出M-HySMap框架，针对脉冲神经网络在网格NoC上的映射问题，通过活动加权多播超图优化，显著减少路由多播跳数并提升优化速度。
中文摘要 AI 辅助
将脉冲神经网络（SNN）映射到神经形态众核平台的过程常被建模为图划分和成对放置成本问题，该抽象虽便捷，但与实际通信事件不匹配：一个源神经元发出的脉冲会被传递到一组突触后目标，且通往多个目标的路径可共享网格链路。本文提出M-HySMap，一种感知路由、活动加权的多播超图映射框架。每个源神经元会针对其突触后扇出生成一条有向超边，权重由已分析的活动决定。映射器从强活动感知图/QAP种子开始，优化不同目标核心扇出、确定性网格路径的并集以及链路拥塞。核心算法观察结果是局部性：移动一个神经元仅会改变其自身以源为根的超边及其前驱的超边，这允许在缓存所有未受影响路径贡献的同时进行精确增量增益评估。我们详细阐述该组合结构，推导保守放置下界，并描述保留最优当前值的划分与放置邻域组合。在115个作业的测试套件（基于Potjans启发的循环SNN，以及4×4至6×6的网格NoC，含7×7压力案例）中，M-HySMap相比Activity+QAP可减少10.6%-19.6%的路由多播跳数，相比Edge+QAP可减少19.7%-41.1%；增量更新使优化速度提升4.7-12.7倍，且与全重计算的数值精度匹配。
英文摘要
Mapping spiking neural networks (SNNs) onto neuromorphic many-core platforms is often formulated with graph partitioning and pairwise placement costs. That abstraction is convenient, but it does not match the physical communication event: one spike from a source neuron is delivered to a set of postsynaptic destinations, and routes to several destinations can share mesh links. We present M-HySMap, a route-aware, activity-weighted multicast hypergraph mapping framework. Each source neuron induces a directed hyperedge to its postsynaptic fanout, weighted by profiled activity. The mapper starts from strong activity-aware graph/QAP seeds and then optimizes distinct destination-core fanout, the union of deterministic mesh routes, and link congestion. The central algorithmic observation is locality: moving one neuron can change only its own source-rooted hyperedge and the hyperedges of its predecessors. This permits exact incremental gain evaluation while caching every unaffected route contribution. We expose this combinatorial structure in detail, derive a conservative placement lower bound, and describe a portfolio of partition and placement neighborhoods that preserves the best incumbent. Across a 115-job evidence suite on Potjans-inspired recurrent SNNs and mesh NoCs from 4 x 4 to 6 x 6, plus a 7 x 7 stress case, M-HySMap reduces routed multicast hops by 10.6-19.6% over Activity+QAP and 19.7-41.1% over Edge+QAP. Incremental updates accelerate refinement by 4.7-12.7x while matching full recomputation to numerical precision.
