Road to scalability for efficient graph search on massively parallel neuromorphic hardware

• URL: http://arxiv.org/abs/2606.28907v1
• Date: Sat, 27 Jun 2026 13:29:43 GMT
• Title: Road to scalability for efficient graph search on massively parallel neuromorphic hardware
• Abstract summary: Efficient computation of shortest paths in weighted graphs is a fundamental problem with many applications.<n>We introduce NEURO-MAPP, a distributed shortest path algorithm designed to use the local computation and network communication available in neuromorphic systems.<n>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.
• Score: 1.732622994788234
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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