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.

翻译：我们提出了一种用于NP难边缘用户分配问题的神经形态求解器，该求解器采用具有赢家通吃机制的吸引子网络，并基于贝叶斯置信传播神经网络框架实现。与以往基于能量的吸引子网络不同，我们的求解器采用动态启发式偏置实时引导分配，并在每个赢家通吃单元中引入"不分配"状态，从而在经验上界的时间步数内实现接近最优的性能。该方法兼容神经形态架构，并可能在能效方面带来改进。