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

arXiv: https://arxiv.org/abs/2602.01294 · HTML: https://mlsanity.com/paper/2602.01294/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2026.

## Built on by (most cited first, 1 of 1)

- [Syn2Logic: End-to-End Neuromorphic Design Automation](https://mlsanity.com/paper/2608.25536/) (2026)

## Abstract (arXiv)

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.