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

TL;DR

This paper introduces a neuromorphic solver for the NP-hard Edge User Allocation problem, leveraging a Bayesian Confidence Propagation Neural Network with dynamic heuristics and a no-allocation state to improve efficiency and performance.

Contribution

It presents a novel neuromorphic approach using BCPNN with dynamic heuristics and a no-allocation state for real-time solutions to the NP-hard problem.

Findings

Achieves near-optimal performance within bounded time steps.

Compatible with neuromorphic hardware architectures.

Potential energy efficiency improvements.

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.