Dynamic Heuristic Neuromorphic Solver for the Edge User Allocation Problem with Bayesian Confidence Propagation Neural Network
Neural and Evolutionary Computing
2026-02-03 v1
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.
Cite
@article{arxiv.2602.01294,
title = {Dynamic Heuristic Neuromorphic Solver for the Edge User Allocation Problem with Bayesian Confidence Propagation Neural Network},
author = {Kecheng Zhang and Anders Lansner and Ahsan Javed Awan and Naresh Balaji Ravichandran and Pawel Herman},
journal= {arXiv preprint arXiv:2602.01294},
year = {2026}
}
Comments
submitted to NICE2026