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arXiv · 2602.01294

Dynamic Heuristic Neuromorphic Solver for the Edge User Allocation Problem with Bayesian Confidence Propagation Neural Network

Kecheng Zhang·Anders Lansner·Ahsan Javed Awan·Naresh Balaji Ravichandran·Pawel Herman
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Abstract

We propose a neuromorphic solver for the NP-hard Edge User Allocation problem using an attractor network with Winner-Takes-All (WTA) mechanism implemented with the Bayesian Confidence Propagation Neural Network (BCPNN) framework. Unlike previous energy-based attractor networks, our solver uses dynamic heuristic biasing to guide allocations in real time and introduces a "no allocation" state to each WTA motif, achieving near-optimal performance with an empirically upper-bounded number of time steps. The approach is compatible with neuromorphic architectures and may offer improvements in energy efficiency.

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Kecheng Zhang, Anders Lansner, Ahsan Javed Awan, Naresh Balaji Ravichandran, Pawel Herman. 2026-02-01. Dynamic Heuristic Neuromorphic Solver for the Edge User Allocation Problem with Bayesian Confidence Propagation Neural Network. https://arxiv.org/abs/2602.01294

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