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

2026-02-01

AI Summary

This work addresses the NP-hard edge user allocation problem by proposing an attractor network approach based on a Bayesian Belief Propagation Neural Network (BCPNN), which integrates a winner-take-all (WTA) mechanism with dynamic heuristic biasing and introduces a “no-allocation” state to overcome limitations inherent in conventional energy-based attractor networks. The method achieves near-optimal solutions within a bounded number of time steps on neuromorphic hardware, effectively balancing real-time performance and allocation quality. Experimental results demonstrate that the proposed scheme significantly improves energy efficiency while maintaining near-optimal solution quality, making it well-suited for low-power edge computing scenarios.

Technology Category

• Machine Learning: Hardware-aware ML
• Search and Optimization: Algorithm Configuration
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

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.

Problem

• Edge User Allocation
• NP-hard
• Neuromorphic Computing
• Resource Allocation
• Attractor Network

Innovation

• neuromorphic computing
• Bayesian Confidence Propagation Neural Network
• Winner-Takes-All
• dynamic heuristic biasing
• Edge User Allocation