Hypergraph modeling of SNNs improves neuron-to-core mapping on neuromorphic hardware by exploiting hyperedge overlap and locality for better partitioning and placement than graph-based methods.
Systematic integration of structural and functional data into multi-scale models of mouse primary visual cortex
2 Pith papers cite this work, alongside 326 external citations. Polarity classification is still indexing.
2
Pith papers citing it
326
external citations · OpenAlex
years
2026 2representative citing papers
GPU algorithm for hypergraph partitioning with size and distinct hyperedge constraints achieves 380x speedup and 1.2-2.0x better connectivity than sequential methods.
citing papers explorer
-
A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware
Hypergraph modeling of SNNs improves neuron-to-core mapping on neuromorphic hardware by exploiting hyperedge overlap and locality for better partitioning and placement than graph-based methods.
-
Hypergraph Partitioning on GPU with Distinct Incident Hyperedges and Size Constraints
GPU algorithm for hypergraph partitioning with size and distinct hyperedge constraints achieves 380x speedup and 1.2-2.0x better connectivity than sequential methods.