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This paper introduces M-HySMap, a novel framework for mapping spiking neural networks (SNNs) onto neuromorphic many-core platforms by utilizing activity-weighted multicast hypergraph mapping. The approach optimizes the routing of spikes from source neurons to multiple postsynaptic destinations, significantly improving communication efficiency by reducing routed multicast hops. Experimental results demonstrate that M-HySMap achieves a 10.6-19.6% reduction in hops compared to existing methods, while also accelerating refinement processes by up to 12.7 times.
M-HySMap slashes routed multicast hops by up to 41.1% while enabling rapid incremental updates for SNN mapping on mesh NoCs.
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.
