Road to scalability for efficient graph search on massively parallel neuromorphic hardware
Abstract
Efficient computation of shortest paths in weighted graphs is a fundamental problem with many applications. Neuromorphic hardware platforms promise massively parallel, efficient computation, changing parallelism tradeoffs. In this work, we introduce NEURO-MAPP (Neuromorphic-based Min-Add Parallel Propagation), a distributed shortest path algorithm designed to use the local computation and network communication available in neuromorphic systems. We provide an optimized implementation of the algorithm on the SpiNNaker 2 platform and evaluate its performance on a selection of synthetic and real-world graphs. These results are compared to Dijkstra's algorithm on a modern CPU. We find that the NEURO-MAPP implementation scales favorably in terms of runtime for many graph types while consuming less energy per shortest-path query than the CPU implementation in almost all cases. These findings highlight the potential of neuromorphic hardware featuring sparse, spike-based communication as a scalable and energy-efficient platform for computation in graph search and related tasks.
Cite
@article{arxiv.2606.28907,
title = {Road to scalability for efficient graph search on massively parallel neuromorphic hardware},
author = {Oskar von Seeler and Elena C. Offenberg and Carlo Michaelis and Tomas Kulvicius and Jannik Luboeinski and Andrew B. Lehr and Christian Tetzlaff},
journal= {arXiv preprint arXiv:2606.28907},
year = {2026}
}